AI & Machine Learning Internship — 1 Month
You'll learn core AI & Machine Learning through structured modules and work on a real live client project alongside senior developers. By the end, you can build confidently and have contributed to something that a real client uses.
40+
live training hours
10
core modules
1–2
live client projects
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for basic 3D orientation. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Codebase Basics
🔒 100+ Sessions • Career-Ready
Core AI & Machine Learning modules
Understand the fundamentals of AI and ML, their differences, and their applications in real-world scenarios.
Topics covered
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Differences between AI, ML, Deep Learning, and Data Science
Types of AI: Narrow AI and General AI concepts
Types of Machine Learning:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Rule-based programming versus machine learning
AI/ML applications in healthcare, banking, education, retail, and business
Overview of the AI/ML project lifecycle
What you'll build
- Identify five real-world problems that can be addressed using AI or ML and classify them according to the appropriate learning approach.
By the end: Interns will understand the basic AI/ML ecosystem, distinguish between major concepts, and identify common real-world applications.
Understand the essential Python programming concepts required for data analysis and machine learning.
Topics covered
Introduction to Python and its applications in AI/ML
Variables, data types, and operators
Conditional statements and loops
Strings and string manipulation
Lists, tuples, sets, and dictionaries
Functions and reusable code
Exception handling
File handling using text, CSV, and JSON files
Introduction to modules and packages
Overview of Jupyter Notebook and Python development tools
Introduction to NumPy and Pandas
What you'll build
- Write a Python program that reads a dataset, performs basic calculations, handles invalid values, and displays a summary of the data.
By the end: Interns will understand Python fundamentals and recognize how Python libraries support data analysis and AI/ML development.
Understand the mathematical and statistical foundations used in machine learning algorithms and data interpretation.
Topics covered
Basic algebra and mathematical functions
Introduction to vectors and matrices
Basic matrix operations
Mean, median, mode, variance, and standard deviation
Introduction to probability
Conditional probability and Bayes' theorem
Probability distributions and normal distribution
Correlation and covariance
Introduction to statistical analysis
Basic derivatives and gradients
Overview of optimization and gradient descent
What you'll build
- Calculate descriptive statistics and correlation for a sample dataset and interpret the results using Python.
By the end: Interns will understand the fundamental mathematical and statistical concepts used to analyze data and train machine learning models.
Understand how data is collected, analyzed, cleaned, and prepared before training a machine learning model.
Topics covered
Types of data: Structured, semi-structured, and unstructured
Common data sources: CSV, Excel, JSON, databases, and APIs
Introduction to data collection and ingestion
Understanding datasets, rows, columns, and data types
Introduction to Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization
Handling missing values and duplicate records
Identifying outliers and inconsistent data
Encoding categorical variables
Feature scaling and normalization
Introduction to feature engineering
Importance of data quality in machine learning
What you'll build
- Analyze a sample dataset, identify data quality issues, clean the records, and prepare the dataset for machine learning.
By the end: Interns will understand the data preparation process and explain why clean, consistent, and relevant data is essential for reliable ML predictions.
Understand supervised learning techniques used to predict numerical values and classify data into categories.
Topics covered
Introduction to supervised learning
Features, labels, training data, and predictions
Regression versus classification
Linear Regression
Logistic Regression
Decision Tree algorithms
Random Forest fundamentals
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM) fundamentals
Classification use cases such as spam detection
Regression use cases such as price prediction
Basic model training and prediction workflow
What you'll build
- Study a house price prediction or spam classification example and identify the dataset features, target variable, algorithm, and expected prediction.
By the end: Interns will understand the fundamentals of supervised learning and identify suitable regression or classification algorithms for common problems.
Understand how machine learning discovers patterns in unlabeled data and how model performance is evaluated.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering concepts
K-Means clustering
Hierarchical clustering fundamentals
Customer segmentation applications
Introduction to anomaly detection
Training and testing datasets
Train-test split and cross-validation concepts
Overfitting and underfitting
Confusion matrix
Accuracy, precision, recall, and F1-score
Mean Absolute Error and Root Mean Squared Error
Importance of selecting suitable evaluation metrics
What you'll build
- Examine a customer segmentation example and a classification evaluation report. Identify the clusters, compare evaluation metrics, and explain the model's performance.
By the end: Interns will understand basic unsupervised learning techniques and explain how suitable evaluation metrics help assess the quality of ML models.
Understand how neural networks learn patterns from data and how deep learning is used for complex AI tasks.
Topics covered
Introduction to Deep Learning
Traditional machine learning versus deep learning
Artificial neurons and neural networks
Input, hidden, and output layers
Weights and biases
Activation functions: ReLU, Sigmoid, and Softmax
Forward propagation and backpropagation concepts
Loss functions and optimization
Epochs, batches, and learning rates
Introduction to TensorFlow/Keras or PyTorch
Overview of image recognition and speech processing applications
What you'll build
- Review a simple neural network example and identify its layers, inputs, outputs, and training process.
By the end: Interns will understand neural network fundamentals and recognize the role of deep learning in image, speech, and other complex data-processing applications.
Understand the basic techniques used to process human language and analyze images using AI.
Topics covered
Natural Language Processing (NLP)
Introduction to NLP
Text cleaning and tokenization
Stop-word removal and stemming concepts
Text representation using Bag of Words and TF-IDF
Text classification and sentiment analysis
Introduction to language models
Computer Vision
Introduction to computer vision
Image representation and preprocessing
Image classification fundamentals
Introduction to Convolutional Neural Networks (CNNs)
Object detection concepts
Applications in facial recognition, medical imaging, and quality inspection
What you'll build
- Compare the workflow of a sentiment analysis application with that of an image classification application. Identify the input data, preprocessing steps, model type, and output.
By the end: Interns will understand the foundational concepts of NLP and computer vision and identify how AI systems process text and image data.
Understand the fundamentals of Generative AI and Large Language Models and their applications in modern AI systems.
Topics covered
Introduction to Generative AI
Traditional AI versus Generative AI
Introduction to Large Language Models (LLMs)
Basic concepts of Transformer architecture
Tokens, embeddings, and context windows
Prompt engineering fundamentals
Zero-shot and few-shot prompting
Text generation, summarization, and question answering
Introduction to Retrieval-Augmented Generation (RAG)
Overview of vector databases and semantic search
Introduction to AI agents and tool calling
Common limitations: hallucinations, bias, and inaccurate responses
Responsible AI and data privacy considerations
What you'll build
- Analyze a document-question-answering workflow and identify how an LLM, prompt, document retrieval process, and supporting knowledge source work together.
By the end: Interns will understand modern Generative AI concepts, recognize common LLM application patterns, and explain the basic purpose of RAG and AI agents.
Understand how AI/ML projects are planned, developed, evaluated, and prepared for practical use.
Topics covered
Identifying an AI/ML problem statement
Understanding business requirements and project objectives
Dataset identification and selection
Data collection and preprocessing workflow
Algorithm selection
Model training and evaluation
Comparing model results
Introduction to model deployment
Overview of APIs for accessing ML models
Introduction to Git and GitHub for version control
Basic testing and error analysis
Responsible AI, privacy, and security considerations
Technical documentation and project presentation
Overview of end-to-end AI/ML project architecture
Examples of real-world AI/ML projects
What you'll build
- Prepare a conceptual project plan for a problem such as house price prediction, customer segmentation, spam detection, sentiment analysis, or document-based question answering. Document the problem statement, dataset, approach, expected output, and evaluation method.
By the end: Interns will understand the stages of an AI/ML project and be able to explain how individual concepts contribute to developing a complete solution.
Live client projects
In the 1-month program you work on one live client project alongside your mentor. These are real projects built for actual client requirements — not practice exercises. You build on the same codebase your seniors are working on.
Live client project
Context Assembly Framework (CAF)
Structured information collection, context normalization, template-based instruction assembly, version tracking, and feedback logging.
PythonStreamlitSQLiteJSON
Live client project
RecruitFlow Intelligence Engine
Bulk resume processing, candidate profiling, Gemini AI integration for resume understanding, MySQL-backed candidate matching and suitability scoring.
PythonGemini AIMySQLNLP
Everything included in your 1-month enrollment
✓
40+ live training hours with senior developers✓
Limited VR orientation sessions (introduction to 3D codebase visualizer)✓
10 core AI & Machine Learning modules with practical exercises✓
1 live client project on an active production codebase✓
Internship completion certificate from WhiteandBox.com✓
Remote, hybrid, or in-office at Bangalore1-Month AI & Machine Learning Internship
Live sessions · Real project · Certificate and offer letter
₹5,000
₹2,500
50% off
1 month · live daily sessions
Want to go deeper into every topic?
The 2–6 month program covers the same 10 modules in significantly more depth — more exercises, more concepts, and 5 additional advanced modules that the 1-month doesn't reach.
AI & Machine Learning Internship — 2 to 6 Months
The same core AI & Machine Learning curriculum, covered in significantly more depth — more exercises, more edge cases, more patterns. Plus five additional modules that take you into advanced concepts, system architectures, and production delivery. With more time comes stronger ability, a deeper portfolio, and real exposure to how a professional team ships software.
120+
live training hours
15+
modules
2–3
live client projects
Everything in the 1-month program is included here. The same 10 core modules — but every one of them goes deeper: more exercises, more business scenarios, more edge cases. On top of that, you get five additional modules that the 1-month doesn't have time to cover.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
Limited VR SessionsBasic Orientation
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
🥽
VR Learning Technology • Limited Sessions
⚠️ Limited VR Sessions
Learning Reimagined with VR • Limited VR Sessions
Includes Limited VR Sessions for project development lab. (100+ sessions & unlimited 2-year revisions available in Career-Ready Flagship).
VR LEARNING PREVIEW • Immersive Technology
⚠️ Limited VR Sessions
🔊 Click video to watch VR preview with audio in fullscreen
⚠️ Limited VR Sessions
🏛️ 3D Architectural Walkthroughs
🔒 100+ Sessions • Career-Ready
Foundation modules — all 10, covered in full depth
Understand the fundamentals of AI and ML, their differences, and their applications in real-world scenarios.
Topics covered
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Differences between AI, ML, Deep Learning, and Data Science
Types of AI: Narrow AI and General AI concepts
Types of Machine Learning:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Rule-based programming versus machine learning
AI/ML applications in healthcare, banking, education, retail, and business
Overview of the AI/ML project lifecycle
What you'll build
- Identify five real-world problems that can be addressed using AI or ML and classify them according to the appropriate learning approach.
By the end: Interns will understand the basic AI/ML ecosystem, distinguish between major concepts, and identify common real-world applications.
Understand the essential Python programming concepts required for data analysis and machine learning.
Topics covered
Introduction to Python and its applications in AI/ML
Variables, data types, and operators
Conditional statements and loops
Strings and string manipulation
Lists, tuples, sets, and dictionaries
Functions and reusable code
Exception handling
File handling using text, CSV, and JSON files
Introduction to modules and packages
Overview of Jupyter Notebook and Python development tools
Introduction to NumPy and Pandas
What you'll build
- Write a Python program that reads a dataset, performs basic calculations, handles invalid values, and displays a summary of the data.
By the end: Interns will understand Python fundamentals and recognize how Python libraries support data analysis and AI/ML development.
Understand the mathematical and statistical foundations used in machine learning algorithms and data interpretation.
Topics covered
Basic algebra and mathematical functions
Introduction to vectors and matrices
Basic matrix operations
Mean, median, mode, variance, and standard deviation
Introduction to probability
Conditional probability and Bayes' theorem
Probability distributions and normal distribution
Correlation and covariance
Introduction to statistical analysis
Basic derivatives and gradients
Overview of optimization and gradient descent
What you'll build
- Calculate descriptive statistics and correlation for a sample dataset and interpret the results using Python.
By the end: Interns will understand the fundamental mathematical and statistical concepts used to analyze data and train machine learning models.
Understand how data is collected, analyzed, cleaned, and prepared before training a machine learning model.
Topics covered
Types of data: Structured, semi-structured, and unstructured
Common data sources: CSV, Excel, JSON, databases, and APIs
Introduction to data collection and ingestion
Understanding datasets, rows, columns, and data types
Introduction to Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization
Handling missing values and duplicate records
Identifying outliers and inconsistent data
Encoding categorical variables
Feature scaling and normalization
Introduction to feature engineering
Importance of data quality in machine learning
What you'll build
- Analyze a sample dataset, identify data quality issues, clean the records, and prepare the dataset for machine learning.
By the end: Interns will understand the data preparation process and explain why clean, consistent, and relevant data is essential for reliable ML predictions.
Understand supervised learning techniques used to predict numerical values and classify data into categories.
Topics covered
Introduction to supervised learning
Features, labels, training data, and predictions
Regression versus classification
Linear Regression
Logistic Regression
Decision Tree algorithms
Random Forest fundamentals
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM) fundamentals
Classification use cases such as spam detection
Regression use cases such as price prediction
Basic model training and prediction workflow
What you'll build
- Study a house price prediction or spam classification example and identify the dataset features, target variable, algorithm, and expected prediction.
By the end: Interns will understand the fundamentals of supervised learning and identify suitable regression or classification algorithms for common problems.
Understand how machine learning discovers patterns in unlabeled data and how model performance is evaluated.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering concepts
K-Means clustering
Hierarchical clustering fundamentals
Customer segmentation applications
Introduction to anomaly detection
Training and testing datasets
Train-test split and cross-validation concepts
Overfitting and underfitting
Confusion matrix
Accuracy, precision, recall, and F1-score
Mean Absolute Error and Root Mean Squared Error
Importance of selecting suitable evaluation metrics
What you'll build
- Examine a customer segmentation example and a classification evaluation report. Identify the clusters, compare evaluation metrics, and explain the model's performance.
By the end: Interns will understand basic unsupervised learning techniques and explain how suitable evaluation metrics help assess the quality of ML models.
Understand how neural networks learn patterns from data and how deep learning is used for complex AI tasks.
Topics covered
Introduction to Deep Learning
Traditional machine learning versus deep learning
Artificial neurons and neural networks
Input, hidden, and output layers
Weights and biases
Activation functions: ReLU, Sigmoid, and Softmax
Forward propagation and backpropagation concepts
Loss functions and optimization
Epochs, batches, and learning rates
Introduction to TensorFlow/Keras or PyTorch
Overview of image recognition and speech processing applications
What you'll build
- Review a simple neural network example and identify its layers, inputs, outputs, and training process.
By the end: Interns will understand neural network fundamentals and recognize the role of deep learning in image, speech, and other complex data-processing applications.
Understand the basic techniques used to process human language and analyze images using AI.
Topics covered
Natural Language Processing (NLP)
Introduction to NLP
Text cleaning and tokenization
Stop-word removal and stemming concepts
Text representation using Bag of Words and TF-IDF
Text classification and sentiment analysis
Introduction to language models
Computer Vision
Introduction to computer vision
Image representation and preprocessing
Image classification fundamentals
Introduction to Convolutional Neural Networks (CNNs)
Object detection concepts
Applications in facial recognition, medical imaging, and quality inspection
What you'll build
- Compare the workflow of a sentiment analysis application with that of an image classification application. Identify the input data, preprocessing steps, model type, and output.
By the end: Interns will understand the foundational concepts of NLP and computer vision and identify how AI systems process text and image data.
Understand the fundamentals of Generative AI and Large Language Models and their applications in modern AI systems.
Topics covered
Introduction to Generative AI
Traditional AI versus Generative AI
Introduction to Large Language Models (LLMs)
Basic concepts of Transformer architecture
Tokens, embeddings, and context windows
Prompt engineering fundamentals
Zero-shot and few-shot prompting
Text generation, summarization, and question answering
Introduction to Retrieval-Augmented Generation (RAG)
Overview of vector databases and semantic search
Introduction to AI agents and tool calling
Common limitations: hallucinations, bias, and inaccurate responses
Responsible AI and data privacy considerations
What you'll build
- Analyze a document-question-answering workflow and identify how an LLM, prompt, document retrieval process, and supporting knowledge source work together.
By the end: Interns will understand modern Generative AI concepts, recognize common LLM application patterns, and explain the basic purpose of RAG and AI agents.
Understand how AI/ML projects are planned, developed, evaluated, and prepared for practical use.
Topics covered
Identifying an AI/ML problem statement
Understanding business requirements and project objectives
Dataset identification and selection
Data collection and preprocessing workflow
Algorithm selection
Model training and evaluation
Comparing model results
Introduction to model deployment
Overview of APIs for accessing ML models
Introduction to Git and GitHub for version control
Basic testing and error analysis
Responsible AI, privacy, and security considerations
Technical documentation and project presentation
Overview of end-to-end AI/ML project architecture
Examples of real-world AI/ML projects
What you'll build
- Prepare a conceptual project plan for a problem such as house price prediction, customer segmentation, spam detection, sentiment analysis, or document-based question answering. Document the problem statement, dataset, approach, expected output, and evaluation method.
By the end: Interns will understand the stages of an AI/ML project and be able to explain how individual concepts contribute to developing a complete solution.
Additional modules — topics the 1-month doesn't reach
Understand how to discover hidden patterns, natural groupings, and underlying structures in unlabeled datasets.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering and its business applications
K-Means clustering
Choosing the number of clusters
Elbow method
Silhouette score
Hierarchical clustering
Agglomerative clustering
Dendrogram interpretation
DBSCAN and density-based clustering
Detecting noise and outliers using clustering concepts
Feature scaling for distance-based algorithms
Distance measures and similarity
Introduction to dimensionality reduction
Principal Component Analysis (PCA)
Explained variance and principal components
Visualizing high-dimensional data
Limitations and interpretation of clustering results
Applications in customer segmentation and behavioral analysis
What you'll build
- Use a customer dataset to build a customer segmentation model using K-Means. Experiment with different cluster counts and visualize the resulting groups.
By the end: Trainees will be able to apply clustering techniques, identify meaningful groups, interpret cluster characteristics, and understand when unsupervised learning is appropriate.
Understand how combining multiple models can improve predictive performance and learn widely used advanced tabular-data algorithms.
Topics covered
Introduction to ensemble learning
Benefits and limitations of ensemble models
Bagging and its principles
Random Forest and feature importance
Boosting and sequential model improvement
AdaBoost fundamentals
Gradient Boosting
XGBoost concepts
LightGBM concepts
CatBoost concepts
Voting classifiers and regressors
Stacking and blending
Comparing single models with ensembles
Hyperparameter tuning for ensemble algorithms
Class imbalance considerations
Feature importance and interpretation
Model complexity and training time
Evaluating ensembles using cross-validation
Choosing an algorithm based on data size and problem requirements
What you'll build
- Train a baseline model and compare it with a Random Forest and a boosting model on the same dataset. Evaluate their performance and document the trade-offs between accuracy, complexity, and training time.
By the end: Trainees will understand common ensemble techniques, compare advanced ML algorithms, and select suitable models for structured or tabular prediction tasks.
Learn how to reduce unnecessary input features, manage high-dimensional datasets, and explain why machine learning models produce particular predictions.
Topics covered
Importance of feature selection
Filter, wrapper, and embedded feature selection methods
Variance thresholding
Correlation-based feature removal
Mutual information
Recursive Feature Elimination (RFE)
Feature selection using model-based importance
Dimensionality reduction versus feature selection
Principal Component Analysis in greater detail
Benefits and limitations of reduced feature spaces
Feature importance in tree-based models
Permutation importance
Introduction to SHAP
Introduction to LIME
Global versus local model explanations
Interpreting individual predictions
Model explainability and transparency
Explainability limitations and misleading interpretations
Selecting features without data leakage
Documenting model decisions for stakeholders
What you'll build
- Use a tabular dataset to compare a model trained on all features with models using selected features. Explain selected predictions using feature importance or a suitable interpretability tool.
By the end: Trainees will be able to reduce unnecessary features, understand dimensionality reduction, and explain model behavior using appropriate interpretation techniques.
Understand time-dependent data and learn how to develop forecasting models for business metrics and other sequential observations.
Topics covered
Introduction to time series data
Time series versus ordinary tabular data
Timestamps, frequency, and time indexes
Trend, seasonality, and residual components
Time series visualization
Resampling and frequency conversion
Rolling windows and moving averages
Lag features and rolling statistics
Stationarity concepts
Autocorrelation and partial autocorrelation
Time-based train-test splitting
Data leakage in forecasting
Baseline forecasting methods
Moving average forecasting
Exponential smoothing concepts
ARIMA fundamentals
Forecasting with regression and ML models
Forecast evaluation using MAE, RMSE, and related metrics
Forecast horizons and prediction intervals
Business applications: demand, inventory, and sales forecasting
What you'll build
- Develop a sales or demand forecasting model using historical time-stamped data. Compare a simple baseline forecast with a suitable forecasting algorithm and evaluate its predictions on a later time period.
By the end: Trainees will understand time series fundamentals, prepare chronological datasets, develop basic forecasting models, and evaluate forecast accuracy without introducing future-data leakage.
Understand the fundamentals of artificial neural networks and learn how deep learning models represent patterns in complex data.
Topics covered
Introduction to deep learning
Traditional ML versus deep learning
Applications of deep learning
Biological inspiration behind artificial neurons
Artificial neurons and weighted inputs
Weights, biases, and activation functions
Perceptron and multilayer perceptron
Input, hidden, and output layers
Common activation functions:
Sigmoid
Tanh
ReLU
Softmax
Loss functions for regression and classification
Forward propagation
Backpropagation concepts
Gradient descent and optimization
Epochs, batches, and learning rates
Introduction to optimizers such as SGD and Adam
Training, validation, and testing neural networks
Overfitting and regularization
Dropout and early stopping
Introduction to TensorFlow/Keras or PyTorch
Understanding computational requirements and model limitations
What you'll build
- Build a simple neural network using TensorFlow/Keras or PyTorch to classify a standard dataset. Experiment with the number of layers or training settings and compare the results.
By the end: Trainees will understand neural network architecture, training fundamentals, loss functions, and optimization, and will be able to build and evaluate a basic deep learning model.
Live client projects
In the extended program you work on 2–3 live client projects across the duration. You're not just doing exercises — you're contributing to software a real client is waiting to receive.
Live client project
Context Assembly Framework (CAF)
Structured information collection, context normalization, template-based instruction assembly, version tracking, and feedback logging.
PythonStreamlitSQLiteJSON
Live client project
RecruitFlow Intelligence Engine
Bulk resume processing, candidate profiling, Gemini AI integration for resume understanding, MySQL-backed candidate matching and suitability scoring.
PythonGemini AIMySQLNLP
Live client project
SRS Vision AI — Smart Project Explainer and Visualizer
SRS document upload, section extraction, structured summaries, module identification, workflow visualization, and document-based search.
PythonDOCX processingMermaid.jsStreamlit
Live client project
SRSGen AI — Automated SRS Generator
Flask web app generating complete SRS documents from client input — with versioning, template mapping, DOCX export, and pytest test suite.
PythonFlaskSQLitepython-docxpytest
Everything included in your 2–6 month enrollment
✓
120+ live training hours with senior developers✓
Limited VR project sessions (interactive debugging & architecture walkthroughs)✓
15+ modules: all 10 core + 5 advanced modules✓
2–3 live client projects with production code review✓
Internship completion certificate + Letter of Recommendation✓
LinkedIn profile optimization & resume review✓
Remote, hybrid, or in-office at Bangalore2–6 Month AI & Machine Learning Internship
Deeper learning · More projects · Stronger portfolio
₹9,000
₹4,500
50% off
2 / 3 / 4 / 6 months · live daily sessions
Ready for the complete career-ready path?
The Career-Ready flagship bundles everything here with the full AI & Machine Learning career course, industry project standards, and 25 AI-powered development modules — 400+ live hours total. The program serious developers choose when they want job outcomes, not just a certificate.
Career-Ready Program — Flagship
The complete path from zero to job-ready. Four programs bundled into one: the full AI & Machine Learning internship, the advanced AI & Machine Learning career course, industry-grade project engineering, and 25+ modules on AI-powered development. Every day is five hours of live training. This is the program for students who want a real outcome — not just a certificate on a shelf.
400+
live training hours
4
full programs
5+
live client projects
How the 400+ hours break down: 5 live hours per day, 30 hours per week, approximately 120 hours per month — plus extra mentorship, modular support sessions, project reviews, and code walkthroughs on top of scheduled training. Over a 3–6 month program, this compounds to 400+ contact hours.
Real Corporate Campus • Active Software Workplace
This is NOT a Classroom.
It is an Office Where 2,000+ Employees Are Working Daily.
Train directly on our live corporate floor where 2,000+ software engineers build real client software daily — zero theoretical classrooms.
2,000+Software Engineers on Floor
Zero ClassroomsLive Development Floor
100+ VR SessionsUnlimited Revision (2 Yrs)
LIVE FLOOR TOUR • White and Box Corporate Workplace (Bengaluru)
✓ Office Floor where 2,000+ Engineers Work Daily
🔊 Click video to watch with audio in fullscreen
Program 1 — AI & Machine Learning Internship (all 15 modules, complete in full depth)
Complete Foundational Track Included
In the Flagship program, you complete all 15 modules of the internship in full depth, building enterprise capabilities before advancing to specialized architecture.
Understand the fundamentals of AI and ML, their differences, and their applications in real-world scenarios.
Topics covered
Introduction to Artificial Intelligence (AI)
Introduction to Machine Learning (ML)
Differences between AI, ML, Deep Learning, and Data Science
Types of AI: Narrow AI and General AI concepts
Types of Machine Learning:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Rule-based programming versus machine learning
AI/ML applications in healthcare, banking, education, retail, and business
Overview of the AI/ML project lifecycle
What you'll build
- Identify five real-world problems that can be addressed using AI or ML and classify them according to the appropriate learning approach.
By the end: Interns will understand the basic AI/ML ecosystem, distinguish between major concepts, and identify common real-world applications.
Understand the essential Python programming concepts required for data analysis and machine learning.
Topics covered
Introduction to Python and its applications in AI/ML
Variables, data types, and operators
Conditional statements and loops
Strings and string manipulation
Lists, tuples, sets, and dictionaries
Functions and reusable code
Exception handling
File handling using text, CSV, and JSON files
Introduction to modules and packages
Overview of Jupyter Notebook and Python development tools
Introduction to NumPy and Pandas
What you'll build
- Write a Python program that reads a dataset, performs basic calculations, handles invalid values, and displays a summary of the data.
By the end: Interns will understand Python fundamentals and recognize how Python libraries support data analysis and AI/ML development.
Understand the mathematical and statistical foundations used in machine learning algorithms and data interpretation.
Topics covered
Basic algebra and mathematical functions
Introduction to vectors and matrices
Basic matrix operations
Mean, median, mode, variance, and standard deviation
Introduction to probability
Conditional probability and Bayes' theorem
Probability distributions and normal distribution
Correlation and covariance
Introduction to statistical analysis
Basic derivatives and gradients
Overview of optimization and gradient descent
What you'll build
- Calculate descriptive statistics and correlation for a sample dataset and interpret the results using Python.
By the end: Interns will understand the fundamental mathematical and statistical concepts used to analyze data and train machine learning models.
Understand how data is collected, analyzed, cleaned, and prepared before training a machine learning model.
Topics covered
Types of data: Structured, semi-structured, and unstructured
Common data sources: CSV, Excel, JSON, databases, and APIs
Introduction to data collection and ingestion
Understanding datasets, rows, columns, and data types
Introduction to Exploratory Data Analysis (EDA)
Descriptive statistics and data visualization
Handling missing values and duplicate records
Identifying outliers and inconsistent data
Encoding categorical variables
Feature scaling and normalization
Introduction to feature engineering
Importance of data quality in machine learning
What you'll build
- Analyze a sample dataset, identify data quality issues, clean the records, and prepare the dataset for machine learning.
By the end: Interns will understand the data preparation process and explain why clean, consistent, and relevant data is essential for reliable ML predictions.
Understand supervised learning techniques used to predict numerical values and classify data into categories.
Topics covered
Introduction to supervised learning
Features, labels, training data, and predictions
Regression versus classification
Linear Regression
Logistic Regression
Decision Tree algorithms
Random Forest fundamentals
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM) fundamentals
Classification use cases such as spam detection
Regression use cases such as price prediction
Basic model training and prediction workflow
What you'll build
- Study a house price prediction or spam classification example and identify the dataset features, target variable, algorithm, and expected prediction.
By the end: Interns will understand the fundamentals of supervised learning and identify suitable regression or classification algorithms for common problems.
Understand how machine learning discovers patterns in unlabeled data and how model performance is evaluated.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering concepts
K-Means clustering
Hierarchical clustering fundamentals
Customer segmentation applications
Introduction to anomaly detection
Training and testing datasets
Train-test split and cross-validation concepts
Overfitting and underfitting
Confusion matrix
Accuracy, precision, recall, and F1-score
Mean Absolute Error and Root Mean Squared Error
Importance of selecting suitable evaluation metrics
What you'll build
- Examine a customer segmentation example and a classification evaluation report. Identify the clusters, compare evaluation metrics, and explain the model's performance.
By the end: Interns will understand basic unsupervised learning techniques and explain how suitable evaluation metrics help assess the quality of ML models.
Understand how neural networks learn patterns from data and how deep learning is used for complex AI tasks.
Topics covered
Introduction to Deep Learning
Traditional machine learning versus deep learning
Artificial neurons and neural networks
Input, hidden, and output layers
Weights and biases
Activation functions: ReLU, Sigmoid, and Softmax
Forward propagation and backpropagation concepts
Loss functions and optimization
Epochs, batches, and learning rates
Introduction to TensorFlow/Keras or PyTorch
Overview of image recognition and speech processing applications
What you'll build
- Review a simple neural network example and identify its layers, inputs, outputs, and training process.
By the end: Interns will understand neural network fundamentals and recognize the role of deep learning in image, speech, and other complex data-processing applications.
Understand the basic techniques used to process human language and analyze images using AI.
Topics covered
Natural Language Processing (NLP)
Introduction to NLP
Text cleaning and tokenization
Stop-word removal and stemming concepts
Text representation using Bag of Words and TF-IDF
Text classification and sentiment analysis
Introduction to language models
Computer Vision
Introduction to computer vision
Image representation and preprocessing
Image classification fundamentals
Introduction to Convolutional Neural Networks (CNNs)
Object detection concepts
Applications in facial recognition, medical imaging, and quality inspection
What you'll build
- Compare the workflow of a sentiment analysis application with that of an image classification application. Identify the input data, preprocessing steps, model type, and output.
By the end: Interns will understand the foundational concepts of NLP and computer vision and identify how AI systems process text and image data.
Understand the fundamentals of Generative AI and Large Language Models and their applications in modern AI systems.
Topics covered
Introduction to Generative AI
Traditional AI versus Generative AI
Introduction to Large Language Models (LLMs)
Basic concepts of Transformer architecture
Tokens, embeddings, and context windows
Prompt engineering fundamentals
Zero-shot and few-shot prompting
Text generation, summarization, and question answering
Introduction to Retrieval-Augmented Generation (RAG)
Overview of vector databases and semantic search
Introduction to AI agents and tool calling
Common limitations: hallucinations, bias, and inaccurate responses
Responsible AI and data privacy considerations
What you'll build
- Analyze a document-question-answering workflow and identify how an LLM, prompt, document retrieval process, and supporting knowledge source work together.
By the end: Interns will understand modern Generative AI concepts, recognize common LLM application patterns, and explain the basic purpose of RAG and AI agents.
Understand how AI/ML projects are planned, developed, evaluated, and prepared for practical use.
Topics covered
Identifying an AI/ML problem statement
Understanding business requirements and project objectives
Dataset identification and selection
Data collection and preprocessing workflow
Algorithm selection
Model training and evaluation
Comparing model results
Introduction to model deployment
Overview of APIs for accessing ML models
Introduction to Git and GitHub for version control
Basic testing and error analysis
Responsible AI, privacy, and security considerations
Technical documentation and project presentation
Overview of end-to-end AI/ML project architecture
Examples of real-world AI/ML projects
What you'll build
- Prepare a conceptual project plan for a problem such as house price prediction, customer segmentation, spam detection, sentiment analysis, or document-based question answering. Document the problem statement, dataset, approach, expected output, and evaluation method.
By the end: Interns will understand the stages of an AI/ML project and be able to explain how individual concepts contribute to developing a complete solution.
Understand how to discover hidden patterns, natural groupings, and underlying structures in unlabeled datasets.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering and its business applications
K-Means clustering
Choosing the number of clusters
Elbow method
Silhouette score
Hierarchical clustering
Agglomerative clustering
Dendrogram interpretation
DBSCAN and density-based clustering
Detecting noise and outliers using clustering concepts
Feature scaling for distance-based algorithms
Distance measures and similarity
Introduction to dimensionality reduction
Principal Component Analysis (PCA)
Explained variance and principal components
Visualizing high-dimensional data
Limitations and interpretation of clustering results
Applications in customer segmentation and behavioral analysis
What you'll build
- Use a customer dataset to build a customer segmentation model using K-Means. Experiment with different cluster counts and visualize the resulting groups.
By the end: Trainees will be able to apply clustering techniques, identify meaningful groups, interpret cluster characteristics, and understand when unsupervised learning is appropriate.
Understand how combining multiple models can improve predictive performance and learn widely used advanced tabular-data algorithms.
Topics covered
Introduction to ensemble learning
Benefits and limitations of ensemble models
Bagging and its principles
Random Forest and feature importance
Boosting and sequential model improvement
AdaBoost fundamentals
Gradient Boosting
XGBoost concepts
LightGBM concepts
CatBoost concepts
Voting classifiers and regressors
Stacking and blending
Comparing single models with ensembles
Hyperparameter tuning for ensemble algorithms
Class imbalance considerations
Feature importance and interpretation
Model complexity and training time
Evaluating ensembles using cross-validation
Choosing an algorithm based on data size and problem requirements
What you'll build
- Train a baseline model and compare it with a Random Forest and a boosting model on the same dataset. Evaluate their performance and document the trade-offs between accuracy, complexity, and training time.
By the end: Trainees will understand common ensemble techniques, compare advanced ML algorithms, and select suitable models for structured or tabular prediction tasks.
Learn how to reduce unnecessary input features, manage high-dimensional datasets, and explain why machine learning models produce particular predictions.
Topics covered
Importance of feature selection
Filter, wrapper, and embedded feature selection methods
Variance thresholding
Correlation-based feature removal
Mutual information
Recursive Feature Elimination (RFE)
Feature selection using model-based importance
Dimensionality reduction versus feature selection
Principal Component Analysis in greater detail
Benefits and limitations of reduced feature spaces
Feature importance in tree-based models
Permutation importance
Introduction to SHAP
Introduction to LIME
Global versus local model explanations
Interpreting individual predictions
Model explainability and transparency
Explainability limitations and misleading interpretations
Selecting features without data leakage
Documenting model decisions for stakeholders
What you'll build
- Use a tabular dataset to compare a model trained on all features with models using selected features. Explain selected predictions using feature importance or a suitable interpretability tool.
By the end: Trainees will be able to reduce unnecessary features, understand dimensionality reduction, and explain model behavior using appropriate interpretation techniques.
Understand time-dependent data and learn how to develop forecasting models for business metrics and other sequential observations.
Topics covered
Introduction to time series data
Time series versus ordinary tabular data
Timestamps, frequency, and time indexes
Trend, seasonality, and residual components
Time series visualization
Resampling and frequency conversion
Rolling windows and moving averages
Lag features and rolling statistics
Stationarity concepts
Autocorrelation and partial autocorrelation
Time-based train-test splitting
Data leakage in forecasting
Baseline forecasting methods
Moving average forecasting
Exponential smoothing concepts
ARIMA fundamentals
Forecasting with regression and ML models
Forecast evaluation using MAE, RMSE, and related metrics
Forecast horizons and prediction intervals
Business applications: demand, inventory, and sales forecasting
What you'll build
- Develop a sales or demand forecasting model using historical time-stamped data. Compare a simple baseline forecast with a suitable forecasting algorithm and evaluate its predictions on a later time period.
By the end: Trainees will understand time series fundamentals, prepare chronological datasets, develop basic forecasting models, and evaluate forecast accuracy without introducing future-data leakage.
Understand the fundamentals of artificial neural networks and learn how deep learning models represent patterns in complex data.
Topics covered
Introduction to deep learning
Traditional ML versus deep learning
Applications of deep learning
Biological inspiration behind artificial neurons
Artificial neurons and weighted inputs
Weights, biases, and activation functions
Perceptron and multilayer perceptron
Input, hidden, and output layers
Common activation functions:
Sigmoid
Tanh
ReLU
Softmax
Loss functions for regression and classification
Forward propagation
Backpropagation concepts
Gradient descent and optimization
Epochs, batches, and learning rates
Introduction to optimizers such as SGD and Adam
Training, validation, and testing neural networks
Overfitting and regularization
Dropout and early stopping
Introduction to TensorFlow/Keras or PyTorch
Understanding computational requirements and model limitations
What you'll build
- Build a simple neural network using TensorFlow/Keras or PyTorch to classify a standard dataset. Experiment with the number of layers or training settings and compare the results.
By the end: Trainees will understand neural network architecture, training fundamentals, loss functions, and optimization, and will be able to build and evaluate a basic deep learning model.
Program 2 — Full AI & Machine Learning Career Course
Understand the fundamentals of Artificial Intelligence (AI), Machine Learning (ML), and their applications in real-world business and technology environments.
Topics covered
Introduction to Artificial Intelligence and its evolution
Definition, scope, and objectives of AI
Types of AI: Narrow AI, General AI, and Superintelligence as a conceptual classification
Introduction to Machine Learning and its importance
Relationship between AI, ML, Deep Learning, and Data Science
Types of Machine Learning:
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Rule-based programming versus machine learning
Real-world AI/ML applications in healthcare, banking, retail, manufacturing, education, and cybersecurity
AI/ML project lifecycle overview
Introduction to responsible AI and common limitations of AI systems
What you'll build
- Identify real-world problems that can be solved using AI or ML and classify sample use cases according to their learning approach.
By the end: Trainees will understand the AI/ML ecosystem, distinguish between major AI concepts, and identify suitable applications for AI/ML solutions.
Build the Python programming skills required to develop data-processing scripts, machine learning models, and AI applications.
Topics covered
Python installation and environment setup
Python syntax, indentation, variables, and data types
Operators and expressions
Conditional statements and loops
Strings and string manipulation
Lists, tuples, sets, and dictionaries
Functions, parameters, return values, and scope
Lambda functions and basic functional programming concepts
List comprehensions
Exception handling using try, except, finally, and raise
File handling and working with text, CSV, and JSON files
Object-oriented programming:
Classes and objects
Constructors
Inheritance
Encapsulation
Polymorphism
Virtual environments and package management with pip
Introduction to Jupyter Notebook and Python IDEs
Writing reusable, readable, and maintainable Python code
What you'll build
- Develop a Python application that reads a CSV file, validates records, handles missing or invalid values, performs calculations, and generates a summary report.
By the end: Trainees will be able to write Python programs, organize code into reusable functions and modules, process files, and prepare for data science and ML development.
Understand the mathematical and statistical concepts required to interpret data, train machine learning models, and evaluate their performance.
Topics covered
Basic arithmetic, algebra, equations, and functions
Variables, constants, and mathematical notation
Linear equations and graphical interpretation
Exponents, logarithms, and their applications in ML
Introduction to vectors and matrices
Matrix operations and matrix multiplication
Dot products and vector representation
Mean, median, mode, range, variance, and standard deviation
Probability fundamentals and probability distributions
Conditional probability and Bayes' theorem
Independent and dependent events
Normal, binomial, and uniform distributions
Sampling, population, and sample statistics
Correlation and covariance
Introduction to statistical hypothesis testing
Null and alternative hypotheses
P-values and confidence intervals at a conceptual level
Introduction to derivatives and gradients
Concept of optimization and gradient descent
How mathematical concepts support model training and prediction
What you'll build
- Use Python, NumPy, and basic statistical functions to calculate descriptive statistics, explore probability distributions, measure correlation, and visualize relationships between variables.
By the end: Trainees will understand the mathematical foundations of ML, interpret statistical summaries, and recognize how probability, linear algebra, and optimization contribute to model development.
Set up a professional Python development environment and learn the essential libraries and tools used in AI/ML projects.
Topics covered
Python environment setup and dependency management
Jupyter Notebook and JupyterLab
VS Code and notebook-based development
Installing and managing Python packages
Introduction to NumPy
Introduction to Pandas
Introduction to Matplotlib and Seaborn
Introduction to scikit-learn
Understanding library documentation and examples
Organizing an AI/ML project directory
Managing configuration files and datasets
Introduction to Git and version control
Git repositories, commits, branches, and merging
Introduction to GitHub for source-code management
Using requirements.txt to record dependencies
Reproducibility, random seeds, and environment consistency
Debugging Python code and handling common errors
What you'll build
- Create a GitHub repository for a Python data analysis project, organize its files, install the required libraries, document the setup, and upload the code.
By the end: Trainees will be able to configure an AI/ML development environment, use essential Python libraries, manage project dependencies, and maintain code using Git and GitHub.
Understand how to collect, import, validate, and store data from different sources for AI/ML applications.
Topics covered
Importance of data in AI/ML systems
Structured, semi-structured, and unstructured data
Data sources: CSV, Excel, JSON, databases, APIs, and public datasets
Reading and writing CSV, Excel, and JSON files using Pandas
Introduction to relational databases and SQL
Connecting Python applications to databases
SQL queries for selecting, filtering, sorting, and aggregating data
Introduction to REST APIs and HTTP requests
Retrieving data from APIs using Python
Basic web data collection concepts and applicable restrictions
Introduction to data ingestion pipelines
Batch data ingestion versus streaming data ingestion
Data formats and schema fundamentals
Data validation and integrity checks
Handling duplicate records and inconsistent formats
Introduction to cloud-based data storage concepts
Data privacy, permissions, and responsible data collection
What you'll build
- Build a Python data ingestion script that imports a dataset from a CSV file or permitted API, validates the records, removes duplicates, and stores the cleaned data in a database.
By the end: Trainees will be able to collect and import data from common sources, perform basic validation, and organize datasets for subsequent analysis and model development.
Learn how to examine datasets, discover patterns, identify anomalies, and derive useful insights before building machine learning models.
Topics covered
Introduction to data analysis and EDA
Understanding dataset structure, rows, columns, and data types
Inspecting datasets using Pandas
Descriptive statistics and frequency distributions
Identifying numerical and categorical variables
Univariate analysis
Bivariate and multivariate analysis
Grouping, aggregation, and summarization
Correlation analysis and correlation matrices
Detecting outliers and unusual observations
Identifying skewness and distribution patterns
Data visualization principles
Histograms, bar charts, line charts, and scatter plots
Box plots and heatmaps
Comparing groups and identifying relationships
Formulating data-driven questions
Interpreting analytical findings
Documenting EDA results and observations
What you'll build
- Perform EDA on a retail sales, customer, or student performance dataset. Generate descriptive statistics, visualizations, correlation analysis, and a report summarizing important findings.
By the end: Trainees will be able to explore datasets systematically, identify patterns and data quality issues, visualize relationships, and determine which features may be useful for machine learning.
Prepare raw data for machine learning by handling missing values, inconsistent formats, categorical variables, outliers, and feature representation.
Topics covered
Importance of data preprocessing
Data quality assessment
Handling missing values using deletion and imputation
Identifying duplicate records
Correcting data types and inconsistent formats
Encoding categorical variables:
Label encoding
One-hot encoding
Feature scaling:
Standardization
Min-max normalization
Handling numerical and categorical features
Outlier detection and treatment strategies
Feature transformation and mathematical transformations
Date and time feature extraction
Text feature preparation fundamentals
Feature selection concepts
Feature extraction concepts
Creating derived features from existing columns
Train-test data leakage and why it matters
Fitting preprocessing steps only on training data
Using scikit-learn preprocessing pipelines
Maintaining consistent preprocessing during training and inference
What you'll build
- Take an imperfect dataset, clean its missing values, encode categorical columns, scale numerical features, and build a preprocessing pipeline using scikit-learn.
By the end: Trainees will be able to transform raw datasets into suitable model inputs, construct reusable preprocessing workflows, and reduce common data preparation errors.
Understand regression algorithms and learn how to predict continuous numerical values from historical data.
Topics covered
Introduction to supervised learning
Features, labels, training data, and prediction
Regression versus classification
Simple Linear Regression
Multiple Linear Regression
Linear regression assumptions and limitations
Cost functions and prediction errors
Introduction to gradient descent for regression
Polynomial Regression
Decision Tree Regression
Random Forest Regression
Introduction to regularization
Ridge and Lasso regression
Training and testing regression models
Prediction using scikit-learn
Regression evaluation metrics:
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
R-squared
Interpreting residuals and model performance
Underfitting and overfitting in regression
Selecting a suitable regression algorithm
What you'll build
- Build a house price prediction or sales forecasting model using historical data. Train at least two regression algorithms and compare their performance using suitable evaluation metrics.
By the end: Trainees will understand regression techniques, train models for numerical prediction, interpret evaluation metrics, and select an appropriate baseline model for a regression problem.
Learn how to build models that assign records to categories and solve practical classification problems.
Topics covered
Introduction to classification
Binary and multiclass classification
Logistic Regression
K-Nearest Neighbors (KNN)
Decision Tree Classification
Random Forest Classification
Support Vector Machines (SVM)
Naive Bayes classification
Training and testing classification models
Decision boundaries and class predictions
Probability estimates and classification thresholds
Confusion matrix
Accuracy, precision, recall, and F1-score
Specificity and sensitivity
ROC curve and Area Under the Curve (ROC-AUC)
Class imbalance and its impact on performance
Baseline models and algorithm comparison
Selecting metrics based on the business problem
Classification use cases: spam detection, churn prediction, and fraud screening
Understanding the limitations of classification models
What you'll build
- Build a spam detection, customer churn prediction, or loan approval classification model. Compare two or more algorithms using a confusion matrix, precision, recall, and F1-score.
By the end: Trainees will be able to train classification models, evaluate predictions using appropriate metrics, and explain how classification algorithms support real-world decision-making.
Learn how to build reliable machine learning workflows, evaluate generalization, and improve model performance through systematic experimentation.
Topics covered
The complete model development lifecycle
Training, validation, and test datasets
Train-test splitting strategies
Cross-validation and K-fold cross-validation
Stratified sampling for classification
Baseline model establishment
Bias-variance trade-off
Underfitting and overfitting
Generalization to unseen data
Hyperparameters versus model parameters
Grid Search and Randomized Search
Hyperparameter tuning using scikit-learn
Model selection and comparison
Learning curves and validation curves
Data leakage and its consequences
Feature selection and its influence on performance
Reproducible experiments
Tracking model configurations and evaluation results
Choosing the final model based on appropriate metrics
Documenting model selection decisions
What you'll build
- Take a classification or regression dataset, train multiple candidate models, apply cross-validation and hyperparameter tuning, and prepare a comparison report identifying the best-performing model.
By the end: Trainees will understand how to validate models correctly, avoid common evaluation mistakes, tune hyperparameters, and select models based on measurable performance.
Understand how to discover hidden patterns, natural groupings, and underlying structures in unlabeled datasets.
Topics covered
Introduction to unsupervised learning
Supervised versus unsupervised learning
Clustering and its business applications
K-Means clustering
Choosing the number of clusters
Elbow method
Silhouette score
Hierarchical clustering
Agglomerative clustering
Dendrogram interpretation
DBSCAN and density-based clustering
Detecting noise and outliers using clustering concepts
Feature scaling for distance-based algorithms
Distance measures and similarity
Introduction to dimensionality reduction
Principal Component Analysis (PCA)
Explained variance and principal components
Visualizing high-dimensional data
Limitations and interpretation of clustering results
Applications in customer segmentation and behavioral analysis
What you'll build
- Use a customer dataset to build a customer segmentation model using K-Means. Experiment with different cluster counts and visualize the resulting groups.
By the end: Trainees will be able to apply clustering techniques, identify meaningful groups, interpret cluster characteristics, and understand when unsupervised learning is appropriate.
Understand how combining multiple models can improve predictive performance and learn widely used advanced tabular-data algorithms.
Topics covered
Introduction to ensemble learning
Benefits and limitations of ensemble models
Bagging and its principles
Random Forest and feature importance
Boosting and sequential model improvement
AdaBoost fundamentals
Gradient Boosting
XGBoost concepts
LightGBM concepts
CatBoost concepts
Voting classifiers and regressors
Stacking and blending
Comparing single models with ensembles
Hyperparameter tuning for ensemble algorithms
Class imbalance considerations
Feature importance and interpretation
Model complexity and training time
Evaluating ensembles using cross-validation
Choosing an algorithm based on data size and problem requirements
What you'll build
- Train a baseline model and compare it with a Random Forest and a boosting model on the same dataset. Evaluate their performance and document the trade-offs between accuracy, complexity, and training time.
By the end: Trainees will understand common ensemble techniques, compare advanced ML algorithms, and select suitable models for structured or tabular prediction tasks.
Learn how to reduce unnecessary input features, manage high-dimensional datasets, and explain why machine learning models produce particular predictions.
Topics covered
Importance of feature selection
Filter, wrapper, and embedded feature selection methods
Variance thresholding
Correlation-based feature removal
Mutual information
Recursive Feature Elimination (RFE)
Feature selection using model-based importance
Dimensionality reduction versus feature selection
Principal Component Analysis in greater detail
Benefits and limitations of reduced feature spaces
Feature importance in tree-based models
Permutation importance
Introduction to SHAP
Introduction to LIME
Global versus local model explanations
Interpreting individual predictions
Model explainability and transparency
Explainability limitations and misleading interpretations
Selecting features without data leakage
Documenting model decisions for stakeholders
What you'll build
- Use a tabular dataset to compare a model trained on all features with models using selected features. Explain selected predictions using feature importance or a suitable interpretability tool.
By the end: Trainees will be able to reduce unnecessary features, understand dimensionality reduction, and explain model behavior using appropriate interpretation techniques.
Understand time-dependent data and learn how to develop forecasting models for business metrics and other sequential observations.
Topics covered
Introduction to time series data
Time series versus ordinary tabular data
Timestamps, frequency, and time indexes
Trend, seasonality, and residual components
Time series visualization
Resampling and frequency conversion
Rolling windows and moving averages
Lag features and rolling statistics
Stationarity concepts
Autocorrelation and partial autocorrelation
Time-based train-test splitting
Data leakage in forecasting
Baseline forecasting methods
Moving average forecasting
Exponential smoothing concepts
ARIMA fundamentals
Forecasting with regression and ML models
Forecast evaluation using MAE, RMSE, and related metrics
Forecast horizons and prediction intervals
Business applications: demand, inventory, and sales forecasting
What you'll build
- Develop a sales or demand forecasting model using historical time-stamped data. Compare a simple baseline forecast with a suitable forecasting algorithm and evaluate its predictions on a later time period.
By the end: Trainees will understand time series fundamentals, prepare chronological datasets, develop basic forecasting models, and evaluate forecast accuracy without introducing future-data leakage.
Understand the fundamentals of artificial neural networks and learn how deep learning models represent patterns in complex data.
Topics covered
Introduction to deep learning
Traditional ML versus deep learning
Applications of deep learning
Biological inspiration behind artificial neurons
Artificial neurons and weighted inputs
Weights, biases, and activation functions
Perceptron and multilayer perceptron
Input, hidden, and output layers
Common activation functions:
Sigmoid
Tanh
ReLU
Softmax
Loss functions for regression and classification
Forward propagation
Backpropagation concepts
Gradient descent and optimization
Epochs, batches, and learning rates
Introduction to optimizers such as SGD and Adam
Training, validation, and testing neural networks
Overfitting and regularization
Dropout and early stopping
Introduction to TensorFlow/Keras or PyTorch
Understanding computational requirements and model limitations
What you'll build
- Build a simple neural network using TensorFlow/Keras or PyTorch to classify a standard dataset. Experiment with the number of layers or training settings and compare the results.
By the end: Trainees will understand neural network architecture, training fundamentals, loss functions, and optimization, and will be able to build and evaluate a basic deep learning model.
Understand how AI systems process images and learn the fundamentals of computer vision and image classification.
Topics covered
Introduction to computer vision
Image representation using pixels, channels, and dimensions
RGB and grayscale images
Image loading and display using Python
Image resizing, cropping, and normalization
Image augmentation concepts
Training and testing datasets for image tasks
Image classification fundamentals
Convolutional Neural Networks (CNNs)
Convolution and filters
Feature maps and pooling
Flattening and dense layers
CNN architecture fundamentals
Transfer learning
Pretrained image classification models
Introduction to object detection
Image segmentation concepts
Evaluation metrics for image classification
Class imbalance in image datasets
Responsible use of image data and privacy considerations
What you'll build
- Build an image classification application using a small, appropriately licensed image dataset. Train a simple CNN or apply transfer learning, then evaluate its predictions on unseen images.
By the end: Trainees will understand image preprocessing, CNN fundamentals, transfer learning, and common computer vision tasks, and will be able to build a basic image classification solution.
Learn how to process and analyze human language using Python and develop foundational text classification and text analysis applications.
Topics covered
Introduction to Natural Language Processing
Challenges in processing human language
Text collection and preparation
Text normalization and cleaning
Tokenization
Lowercasing and punctuation handling
Stop-word removal and its limitations
Stemming and lemmatization
N-grams and text features
Bag-of-Words representation
TF-IDF vectorization
Text classification using scikit-learn
Sentiment analysis fundamentals
Spam detection and topic categorization
Word embeddings and semantic representation
Introduction to recurrent neural networks and LSTMs
Introduction to Transformer-based NLP
Language models and their capabilities
Evaluating text classification systems
Handling ambiguous language, bias, and sensitive text data
What you'll build
- Develop a sentiment analysis or spam classification application using Python and scikit-learn. Compare a simple text representation with TF-IDF and evaluate the resulting model.
By the end: Trainees will be able to preprocess text, convert language into numerical features, train basic NLP models, and understand the foundations of modern language-processing systems.
Understand how an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.
Topics covered
Introduction to reinforcement learning
Supervised, unsupervised, and reinforcement learning comparison
Agent, environment, state, action, and reward
Policies and value functions
Immediate and cumulative rewards
Return and discount factor
Exploration versus exploitation
Markov Decision Process (MDP)
State transition concepts
Q-learning fundamentals
Q-tables and action selection
Epsilon-greedy strategies
Learning from repeated interactions
Policy-based and value-based methods at a conceptual level
Introduction to Deep Q-Networks
Reward design and its challenges
Evaluation of reinforcement learning agents
Applications in robotics, scheduling, and simulation
Safety, unintended behavior, and practical limitations
What you'll build
- Implement a simple Q-learning agent in a small grid-world or similar simulated environment. Observe how its decisions change as it receives rewards and penalties.
By the end: Trainees will understand the core concepts of reinforcement learning and implement a basic learning agent in a controlled simulation.
Understand generative AI, large language models, and the principles behind AI systems that generate text, code, summaries, and other forms of content.
Topics covered
Introduction to Generative AI
Discriminative versus generative models
Common generative AI applications
Introduction to foundation models
Large Language Models and their capabilities
Transformer architecture at a conceptual level
Tokens, tokenization, and context windows
Embeddings and semantic representations
Attention mechanisms and their purpose
Pretraining and instruction tuning
Fine-tuning versus prompting
Prompt engineering fundamentals
Zero-shot, one-shot, and few-shot prompting
System instructions and structured prompts
Controlling output formats
Temperature and other generation settings
Common LLM limitations and hallucinations
Context management and long-input limitations
Evaluating generated responses
Privacy, bias, copyright, and responsible AI use
Overview of hosted model APIs and locally run models
Introduction to open-source model ecosystems
What you'll build
- Build a Python application that uses an available LLM interface to summarize text, answer questions about supplied content, or generate structured output. Test different prompts and compare the accuracy and consistency of responses.
By the end: Trainees will understand how LLM-based applications work, write effective prompts, integrate a model into Python, and evaluate generated outputs while accounting for hallucinations and other limitations.
Learn how to build AI applications that answer questions using external documents or knowledge sources instead of relying solely on a language model's internal knowledge.
Topics covered
Limitations of standalone LLMs
Introduction to Retrieval-Augmented Generation (RAG)
RAG architecture and workflow
Document ingestion and extraction
Processing PDF, TXT, and other supported document formats
Document cleaning and normalization
Chunking strategies and chunk overlap
Generating text embeddings
Semantic search fundamentals
Vector similarity and distance measures
Introduction to vector databases
Vector indexing and metadata filtering
Overview of vector storage options such as FAISS, Chroma, and managed vector databases
Retrieval strategies and top-k results
Building context from retrieved documents
Prompt construction for grounded answers
Source attribution and citations in generated responses
Retrieval quality and relevance evaluation
RAG failure modes and hallucination reduction
Access control and handling confidential documents
Introduction to hybrid search and reranking
Overview of RAG evaluation methods
What you'll build
- Develop a document-question-answering application in Python. Ingest a set of permitted documents, create embeddings, retrieve relevant passages, generate answers grounded in those passages, and display supporting source references.
By the end: Trainees will understand RAG architecture, implement document retrieval and generation workflows, and evaluate whether responses are supported by the supplied knowledge sources.
Understand how LLM-powered applications can use tools, follow multi-step workflows, and coordinate tasks while remaining subject to defined controls.
Topics covered
Introduction to AI agents
Differences between a chatbot, workflow, and agent
Agent components: model, instructions, tools, state, and control logic
Tool calling and function calling
Structured inputs and outputs
Planning and task decomposition concepts
Multi-step execution and intermediate results
State and conversation context management
Workflow orchestration
Introduction to agent frameworks such as LangChain and LangGraph
Connecting Python functions and approved APIs as tools
Retrieval tools and document-search integration
Error handling and retry strategies
Human-in-the-loop review and approval
Tool permissions and least-privilege access
Preventing uncontrolled actions and limiting execution
Evaluating task completion and tool-use accuracy
Observability and execution tracing
Risks from prompt injection and untrusted content
Use cases: document assistants, research support, and internal workflow automation
What you'll build
- Build a controlled Python-based assistant that receives a request, calls one or more approved tools, summarizes the results, and requests human confirmation before any consequential action.
By the end: Trainees will understand agentic workflows, implement basic tool-using AI applications, and apply appropriate safeguards to prevent unsafe or unintended actions.
Learn how to convert trained machine learning models and AI capabilities into application services that other software systems can access.
Topics covered
From notebooks to usable applications
Application architecture for AI/ML systems
Model inference and prediction workflows
Introduction to HTTP and REST API concepts
API methods, endpoints, requests, and responses
JSON request and response formats
Building APIs using FastAPI or Flask
Input validation and schema definitions
Loading saved models into an application
Creating prediction endpoints
Integrating LLM APIs into Python applications
Database fundamentals and SQL integration
Storing inputs, outputs, and prediction metadata
Configuration management and environment variables
API keys and secret handling
Error handling and meaningful response codes
Logging requests and application errors
API testing using appropriate tools
Authentication and basic authorization concepts
Rate limiting and request validation
Separating application code, model logic, and data access
Introduction to simple web interfaces for AI applications
What you'll build
- Create a Python API that loads a previously trained ML model, accepts validated JSON input, returns a prediction, and records selected request and result details in a database.
By the end: Trainees will be able to expose ML models through APIs, integrate AI functionality into applications, handle structured inputs and outputs, and implement basic application-level controls.
Understand how machine learning models are packaged, deployed, monitored, versioned, and maintained after development.
Topics covered
Introduction to model deployment
Training versus inference environments
Batch inference versus real-time inference
Saving and loading models using suitable formats
Model serialization with joblib or other appropriate tools
Packaging Python applications and dependencies
Introduction to Docker and container fundamentals
Running an AI/ML API inside a container
Deployment options: local server, virtual machine, container service, and managed platform
Overview of cloud AI/ML deployment concepts
Introduction to MLflow for experiment tracking and model management
Tracking parameters, metrics, and artifacts
Model registry and versioning concepts
Dataset and model version management
CI/CD fundamentals for ML applications
Automated testing for model and application code
Model monitoring and application logging
Detecting data drift and concept drift
Monitoring prediction quality when labels become available
Model retraining and deployment strategies
Rollback and version recovery
Reproducibility and deployment documentation
What you'll build
- Package a trained ML model behind a Python API, containerize the application using Docker, and deploy it to an available local or approved cloud environment. Record the model version and basic performance information.
By the end: Trainees will understand the model lifecycle, deploy a basic ML application, track experiments and versions, and identify the monitoring and maintenance requirements of production-oriented systems.
Develop the skills required to evaluate AI/ML solutions for reliability, fairness, security, privacy, performance, and operational suitability.
Topics covered
Responsible AI principles
Fairness and bias in datasets and predictions
Data privacy and personally identifiable information
Data consent and appropriate data handling
Model transparency and explainability
Reliability and robustness
Testing data pipelines and preprocessing logic
Unit testing Python functions
API integration and end-to-end testing
Model evaluation beyond accuracy
Error analysis and examination of incorrect predictions
Sensitivity to input changes
Testing edge cases and invalid inputs
LLM evaluation: relevance, groundedness, and consistency
Prompt injection and insecure tool-use risks
Handling untrusted documents and user inputs
Secret management and access controls
Dependency and application security fundamentals
Performance profiling and execution-time measurement
Memory usage and computational cost
Inference latency and throughput
Caching and batching concepts
Cost-aware model and API selection
Logging, monitoring, incident handling, and documentation
What you'll build
- Perform a quality review of an existing ML or LLM application. Test invalid inputs, inspect incorrect predictions, measure response time, check for sensitive-data exposure, and prepare a report listing risks and recommended improvements.
By the end: Trainees will be able to test AI/ML applications systematically, identify common risks, interpret performance and quality issues, and recommend practical improvements before deployment.
Apply the concepts learned throughout the curriculum to design, implement, evaluate, and demonstrate a complete AI/ML solution based on a defined real-world problem.
Topics covered
Identifying and defining a business problem
Understanding stakeholder requirements
Defining the project scope and measurable success criteria
Identifying data sources and checking data suitability
Designing the solution architecture
Preparing the development environment and repository
Collecting, validating, and exploring the data
Performing data preprocessing and feature engineering
Selecting suitable algorithms and establishing a baseline
Training and comparing candidate models
Performing model evaluation and error analysis
Improving performance through suitable tuning
Documenting model limitations and assumptions
Saving the selected model and required preprocessing steps
Building an API or user-facing interface
Integrating a database when required
Implementing validation and error handling
Testing functionality and model performance
Applying responsible AI and security considerations
Deploying the application to an approved environment
Recording model and application versions
Preparing technical documentation and a user guide
Creating a project presentation and demonstration
Explaining design decisions, limitations, and future improvements
What you'll build
- Complete one end-to-end project selected from an appropriate problem category, such as:
- Predictive Analytics:** House price prediction, sales forecasting, or demand prediction
- Classification:** Customer churn prediction, spam detection, or loan risk classification
- Unsupervised Learning:** Customer segmentation or anomaly exploration
- Computer Vision:** Image classification using a suitable dataset
- NLP:** Sentiment analysis or text classification
- Generative AI:** Document summarization or a question-answering assistant
- RAG Application:** Knowledge assistant that retrieves information from supplied documents
- AI Application Development:** A Python API that integrates an ML model into a usable application
- The project should include a problem statement, dataset, implementation, evaluation, testing, documentation, and a working demonstration. Deployment requirements should match the selected project and the course duration.
By the end: Trainees will demonstrate their ability to take an AI/ML project from problem definition through implementation and evaluation to a working application. They will produce project documentation, explain their technical decisions, and demonstrate the solution to reviewers or stakeholders.
Program 3 — Industry Project Engineering Standards (25 modules)
The complete professional software engineering curriculum — from requirements through deployment, monitoring, and client handover. Every module produces a real deliverable. By the end, you have shipped a complete application the way a consulting firm would: requirements signed off, code tested, system documented, and ownership transferred.
Understand the client's business problem and convert it into clear, actionable requirements for an AI/ML solution.
Topics covered
Understanding client requirements and business objectives
Identifying the problem AI/ML needs to solve
Defining functional and non-functional requirements
Identifying stakeholders, users, and expected outcomes
Defining project scope, constraints, and deliverables
Evaluating feasibility, risks, budget, and timelines
Preparing the Software Requirements Specification (SRS)
Defining project success criteria and acceptance conditions
What you'll build
- Analyse a client requirement for a customer churn prediction system and prepare an SRS covering the business problem, required inputs, expected predictions, user roles, constraints, and acceptance criteria.
By the end: Evaluating feasibility, risks, budget, and timelines Preparing the Software Requirements Specification (SRS) Defining project success criteria and acceptance conditions Practical Exercise:** Analyse a client requirement for a customer churn prediction system and prepare an SRS covering the business problem, required inputs, expected predictions, user roles, constraints, and acceptance criteria. Expected Outcome:** A client-approved project scope and requirement document that guides the complete development process.
Design the technical architecture and select appropriate technologies based on the client's requirements.
Topics covered
Analysing project requirements before selecting technologies
Designing the overall AI/ML application architecture
Selecting Python libraries and ML frameworks
Choosing between traditional ML, deep learning, and LLM-based solutions
Selecting backend frameworks such as FastAPI or Flask
Choosing relational databases, document stores, or vector databases
Identifying external APIs and third-party integrations
Planning frontend, backend, database, and model communication
Evaluating cloud, on-premises, and hybrid deployment options
Documenting architecture decisions and technology dependencies
What you'll build
- Design the architecture for a client-facing fraud detection application, select the ML framework, backend, database, and deployment environment, and document how the components communicate.
By the end: A documented project architecture and technology stack aligned with the client's functional, security, performance, and budget requirements.
Connect the project to the client's approved data sources and establish reliable data ingestion.
Topics covered
Identifying client data sources and access permissions
Connecting to SQL databases
Integrating REST APIs and external services
Importing CSV, Excel, JSON, and Parquet files
Accessing cloud storage and approved document repositories
Handling authentication and credentials securely
Building reusable data ingestion components
Implementing pagination, retries, and error handling
Logging ingestion status and failures
Validating incoming data against expected schemas
What you'll build
- Build a data ingestion component for a client sales forecasting application that imports historical sales data from a database and CSV files, validates the input structure, and records ingestion failures.
By the end: A reusable and reliable data ingestion layer that retrieves approved client data and prepares it for downstream processing.
Develop a repeatable preprocessing pipeline that converts client data into a consistent format suitable for model development.
Topics covered
Assessing data completeness, accuracy, and consistency
Handling missing values and duplicate records
Correcting invalid values and inconsistent categories
Detecting outliers and anomalous records
Transforming categorical and numerical data
Normalizing and scaling features where appropriate
Validating data types and business rules
Building reusable preprocessing pipelines
Preventing data leakage
Recording data quality issues and preprocessing decisions
What you'll build
- Implement a preprocessing pipeline for client customer data that identifies invalid records, handles missing values, standardizes categorical fields, and produces a validated dataset for churn prediction.
By the end: A reusable preprocessing pipeline with documented data quality rules and validation results.
Analyse client data and prepare meaningful features that support the selected business use case.
Topics covered
Analysing data distributions and relationships
Identifying patterns, trends, and anomalies
Investigating class imbalance
Evaluating feature relevance
Creating business-specific features
Extracting date-based and transaction-based features
Performing feature selection
Identifying potential data leakage
Documenting assumptions and analytical findings
Preparing datasets for training and evaluation
What you'll build
- Analyse a client's transaction dataset and prepare features such as transaction frequency, average transaction amount, and unusual activity indicators for a fraud detection model.
By the end: A documented feature dataset and analysis report that support the chosen modelling approach.
Develop an initial ML model that establishes a measurable performance baseline for the client's business problem.
Topics covered
Translating business requirements into an ML task
Identifying classification, regression, clustering, or forecasting requirements
Selecting suitable baseline algorithms
Splitting data into training, validation, and test sets
Building reproducible training pipelines
Establishing baseline evaluation metrics
Recording model configurations and results
Identifying limitations of the baseline model
Comparing baseline performance with business expectations
What you'll build
- Build a baseline customer churn classification model using scikit-learn and record its evaluation metrics against predefined client acceptance criteria.
By the end: A reproducible baseline model with documented performance, assumptions, and improvement opportunities.
Select and optimize a model that meets the client's accuracy, reliability, latency, and operational requirements.
Topics covered
Comparing suitable algorithms
Performing cross-validation
Hyperparameter optimization
Evaluating precision, recall, F1-score, ROC-AUC, MAE, RMSE, or other relevant metrics
Addressing class imbalance
Managing overfitting and underfitting
Selecting decision thresholds based on business costs
Evaluating inference speed and resource consumption
Comparing performance against acceptance criteria
Documenting model selection decisions
What you'll build
- Compare multiple fraud detection models and select a candidate based on recall, false-positive rate, inference latency, and the client's operational requirements.
By the end: A selected and evaluated model with documented evidence that supports its suitability for the intended business use case.
Integrate specialized ML or deep learning models when the client's requirements cannot be adequately met by a basic ML approach.
Topics covered
Evaluating whether a specialized model is necessary
Integrating pretrained models
Building deep learning pipelines with PyTorch or TensorFlow
Implementing transfer learning where appropriate
Integrating NLP models for text-based requirements
Integrating computer vision models for image-based requirements
Selecting forecasting approaches for time-series data
Managing model dependencies and artifacts
Evaluating performance on client-specific data
Comparing specialized models against simpler alternatives
What you'll build
- For a manufacturing client, integrate a suitable pretrained image classification model to identify potential product defects and compare its performance with the project's initial approach.
By the end: A specialized model integrated into the project where justified by business requirements and validated against representative data.
Integrate large language models into client applications when natural-language processing or content generation is required.
Topics covered
Evaluating whether an LLM is appropriate for the use case
Integrating hosted LLM APIs or locally deployed models
Managing prompts and structured model outputs
Validating generated JSON against defined schemas
Implementing tool calling and function integration
Handling model errors, timeouts, and rate limits
Managing token usage, latency, and inference costs
Applying input validation and output safeguards
Protecting confidential client information
Evaluating answer quality and hallucination risks
What you'll build
- Integrate an LLM into a client document-processing application to extract specified fields from business documents and return validated JSON for downstream processing.
By the end: A controlled LLM integration that meets the client's functional requirements and includes validation, error handling, and appropriate data protection.
Develop a document-grounded question-answering system when the client needs answers based on internal documents or domain-specific knowledge.
Topics covered
Connecting to approved document sources
Extracting text from PDFs, Word documents, and other supported formats
Cleaning and chunking document content
Generating embeddings
Integrating vector databases such as FAISS, Chroma, Qdrant, or pgvector
Implementing similarity search and metadata filtering
Building retrieval and context assembly pipelines
Integrating LangChain or LlamaIndex where appropriate
Generating answers grounded in retrieved content
Providing source references and evaluating retrieval quality
What you'll build
- Build a RAG application for a client that answers questions from internal policy documents and returns relevant document references with each answer.
By the end: A document-grounded retrieval application that can search approved client information and provide traceable answers.
Expose trained models and AI functionality through APIs that can be integrated with client applications.
Topics covered
Designing API endpoints around business requirements
Building APIs with FastAPI or Flask
Defining request and response schemas
Integrating model inference into API routes
Validating incoming data with Pydantic or equivalent tools
Implementing authentication and authorization
Handling exceptions, timeouts, and invalid inputs
Implementing structured logging
Documenting endpoints and API contracts
Testing APIs with Postman and automated tests
What you'll build
- Develop a FastAPI service that accepts customer information, executes the selected churn prediction model, and returns a validated prediction with relevant metadata.
By the end: A documented, testable backend API that exposes the project's AI/ML functionality to authorized client applications.
Integrate application and model workflows with the client's data storage requirements.
Topics covered
Designing database schemas
Integrating PostgreSQL, MySQL, or an existing client database
Connecting Python applications using SQLAlchemy or database drivers
Implementing CRUD operations
Storing prediction requests and results
Managing model and prediction metadata
Implementing transactions and database error handling
Handling schema changes and migrations
Applying access controls and data retention rules
Maintaining audit records where required
What you'll build
- Integrate a PostgreSQL database into a prediction application to store authorized prediction requests, results, timestamps, and model version information.
By the end: A database-integrated AI/ML application with reliable persistence and appropriate access and data management controls.
Connect AI/ML functionality to an interface that supports the client's intended business workflow.
Topics covered
Mapping business requirements to user workflows
Selecting an interface approach based on the project
Integrating APIs with a web frontend
Building interfaces with React or an appropriate existing frontend
Using Streamlit for suitable internal tools and prototypes
Implementing data input, validation, and result presentation
Displaying prediction results and confidence information where appropriate
Implementing loading states and error messages
Applying role-based interface access where required
Validating the end-to-end user workflow
What you'll build
- Build a client-facing interface for a demand forecasting API where authorized users can submit data, view forecasts, and inspect historical prediction results.
By the end: A functional interface integrated with the AI/ML backend that supports the client's required user workflow.
Connect the AI/ML application to external systems required by the client's operational workflow.
Topics covered
Integrating external REST APIs
Implementing API authentication
Managing credentials and secrets
Handling rate limits and retries
Validating external responses
Integrating email, notification, or business services where required
Connecting to CRM, ERP, or ticketing systems when applicable
Handling partial failures and service outages
Maintaining integration logs
Testing external-system dependencies
What you'll build
- Integrate a customer support classification service with a ticketing API so that approved predictions can be attached to support tickets for review.
By the end: A reliable integration between the AI/ML application and the external services required by the client's workflow.
Implement controlled AI-powered workflows when the client requires multiple coordinated reasoning or tool-execution steps.
Topics covered
Identifying workflows suitable for agent-based execution
Designing bounded agent responsibilities
Integrating tools and APIs
Managing workflow state and context
Implementing multi-step execution
Using LangGraph or similar orchestration tools when appropriate
Adding human approval checkpoints
Applying tool permissions and execution limits
Handling failures, retries, and incomplete results
Logging and evaluating agent actions
What you'll build
- Develop a support-ticket workflow that categorizes incoming tickets, retrieves relevant knowledge, drafts a response, and requires an authorized employee to approve it before sending.
By the end: A controlled AI workflow integrated into the client's business process with defined permissions, error handling, and human oversight.
Verify that the integrated application, data pipelines, APIs, and models satisfy client requirements.
Topics covered
Unit testing with pytest
Testing data preprocessing functions
Testing API request and response contracts
Integration testing across application components
Testing invalid inputs and edge cases
Validating data schemas and business rules
Testing model predictions against expected behaviour
Regression testing after code or model changes
Evaluating model quality against acceptance thresholds
Maintaining automated test reports
What you'll build
- Build an automated test suite for the client's prediction service that validates preprocessing, API behaviour, database integration, invalid inputs, and minimum model-quality requirements.
By the end: An automated testing suite that detects functional defects and verifies model and application behaviour before release.
Protect client data and ensure that AI/ML functionality complies with applicable security, privacy, and business requirements.
Topics covered
Authentication and authorization
Role-based access control
Secure credential and secret management
Data encryption in transit and at rest where required
Personal and confidential data handling
Data minimization and retention policies
API rate limiting and input validation
Prompt injection and unsafe-output risks for LLM applications
Evaluating bias and inappropriate model behaviour
Audit logging and traceability
Reviewing applicable client policies and legal requirements
What you'll build
- Perform a security review of an AI-powered client application, identify exposed secrets, unauthorized access paths, sensitive-data risks, and unsafe LLM inputs, then implement appropriate controls.
By the end: A documented security review and an application with controls appropriate to the client's data, access, and risk requirements.
Make model outputs understandable and useful to the client's business stakeholders.
Topics covered
Interpreting model predictions
Identifying influential features
Using explainability techniques such as SHAP where appropriate
Presenting confidence and uncertainty responsibly
Explaining model limitations
Comparing predictions with actual outcomes
Preparing model evaluation reports
Building business-oriented visualizations
Creating dashboards using appropriate reporting tools
Documenting assumptions and known limitations
What you'll build
- Develop an explainability report for a customer churn model showing overall performance, important features, and the factors influencing selected predictions.
By the end: A business-readable report that helps stakeholders interpret model behaviour and understand its limitations.
Optimize the AI/ML application to meet client expectations for response time, workload, resource usage, and operating cost.
Topics covered
Measuring API latency and throughput
Profiling Python code
Optimizing data processing and inference
Implementing batch prediction where suitable
Evaluating model size and resource requirements
Managing CPU and GPU utilization
Implementing caching where appropriate
Handling concurrent requests
Optimizing LLM token usage and API costs
Comparing performance against service-level requirements
What you'll build
- Profile a deployed prediction API, measure response latency under representative workloads, identify bottlenecks, and implement improvements while preserving model quality.
By the end: A measured and optimized application that better satisfies the client's performance and cost requirements.
Maintain reproducibility and traceability across model experiments and releases.
Topics covered
Tracking model parameters and evaluation metrics
Recording training datasets and preprocessing configurations
Versioning model artifacts
Comparing experiments
Managing model registries
Recording code and dependency versions
Reproducing previous training runs
Tracking model promotion between environments
Documenting model changes
Supporting rollback to an approved model version
What you'll build
- Integrate MLflow into a client ML project to record training experiments, compare evaluation metrics, register a selected model, and document its release version.
By the end: A reproducible model development workflow with traceable experiments and controlled model version management.
Automate repeatable data processing, training, evaluation, and related project workflows.
Topics covered
Designing modular data and ML pipelines
Scheduling recurring data ingestion
Automating preprocessing and feature generation
Coordinating training and evaluation steps
Implementing task dependencies
Handling retries and failed tasks
Recording pipeline execution status
Validating data before training
Integrating experiment tracking
Supporting controlled retraining and model promotion
What you'll build
- Implement a scheduled demand forecasting pipeline that ingests updated sales data, validates it, generates features, trains or evaluates the model, and records results for review.
By the end: A repeatable and observable pipeline that reduces manual operations and supports reliable project execution.
Package the AI/ML application so that it runs consistently across development, testing, and production environments.
Topics covered
Creating Docker images
Writing Dockerfiles
Managing Python dependencies
Configuring environment variables
Separating development and production settings
Managing application secrets securely
Packaging model artifacts
Configuring database and API connections
Implementing container health checks
Running and debugging containers
What you'll build
- Containerize the client's FastAPI model-serving application, configure its database connection through environment variables, and verify that it runs consistently in a clean environment.
By the end: A reproducibly packaged application with documented configuration and deployment requirements.
Deploy the application to an appropriate client environment and automate release workflows.
Topics covered
Selecting a suitable cloud or on-premises deployment environment
Deploying APIs and application services
Managing model and data artifacts
Configuring networking and access permissions
Setting up environment-specific configuration
Building CI/CD workflows with GitHub Actions or equivalent tools
Automating tests before release
Building and publishing container images
Implementing controlled deployments
Planning rollback and recovery procedures
What you'll build
- Configure a CI/CD workflow that runs automated tests, builds a Docker image, and deploys an approved version of the AI/ML API to a test environment before production release.
By the end: A deployed application and repeatable release workflow aligned with the client's infrastructure and approval process.
Monitor the live application and maintain model reliability as data, usage, and business conditions change.
Topics covered
Monitoring application health and availability
Tracking API latency and error rates
Monitoring resource consumption
Tracking input-data distributions
Detecting data drift and concept drift
Monitoring model quality when actual outcomes become available
Tracking LLM response quality and costs where applicable
Configuring alerts and operational dashboards
Investigating production incidents
Defining retraining and rollback procedures
Managing model and application updates
What you'll build
- Configure monitoring for a deployed prediction service, establish alerts for API failures and data drift, and document the process for investigating performance degradation and approving a model update.
By the end: A monitored production application with defined maintenance, incident response, and model update procedures.
Complete the client project, demonstrate that it meets agreed requirements, and transfer the necessary technical and operational information.
Topics covered
Verifying requirements against delivered functionality
Executing user acceptance testing
Demonstrating end-to-end workflows
Validating agreed model and application acceptance criteria
Documenting known limitations and unresolved issues
Preparing architecture and API documentation
Documenting deployment and configuration procedures
Providing test results and model evaluation reports
Preparing operational and maintenance instructions
Conducting knowledge transfer and handover
Recording client feedback and final sign-off
What you'll build
- Prepare a complete handover package for the client churn prediction system, including a working deployment, architecture diagram, API documentation, test report, model evaluation report, setup instructions, and acceptance checklist. Demonstrate the system against the agreed requirements.
By the end: A client-ready AI/ML solution with verified functionality, documented limitations, deployment instructions, and a formal handover package.
Program 4 — Leveraging AI for 10× Faster Software Development (29 modules)
29 hands-on modules that take you through the complete development lifecycle — from requirement gathering to deployment — using AI coding tools at every stage. All premium tools (ChatGPT-4, Gemini Advanced, GitHub Copilot, Cursor, and more) are configured free on your laptop. Value: ₹15,000+. You use them throughout the program, not just here — and they stay yours.
Establish reusable master prompts that guide AI coding assistants throughout the AI/ML development lifecycle, reducing repeated instructions and improving consistency.
Topics covered
Structuring master prompts with AI/ML project objectives, datasets, technology stacks, and expected deliverables.
Defining AI responsibilities for data preprocessing, exploratory data analysis, model development, evaluation, debugging, and documentation.
Specifying Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
Defining coding conventions, project structure, and model evaluation requirements.
Instructing AI to preserve existing functionality when modifying code.
Requiring verification of generated code, model outputs, and evaluation metrics.
Reusing project-specific instructions across AI-assisted development sessions.
What you'll build
- Create a master prompt for a customer churn prediction application.
- Configure AI to guide the developer through data analysis, preprocessing, model training, evaluation, and testing.
- Ask AI to generate reusable Python functions while following the project's coding standards.
- Compare development workflows with and without reusable project instructions.
By the end: A reusable AI/ML development master prompt that reduces repeated explanations, improves code consistency, and accelerates implementation.
Enable AI coding assistants to understand the complete AI/ML project structure, datasets, dependencies, model workflows, and existing implementation before making changes.
Topics covered
Automated analysis of AI/ML project folders and file structures.
Identifying Python entry points, configuration files, dependencies, notebooks, and scripts.
Understanding the relationships between data ingestion, preprocessing, feature engineering, training, and evaluation.
Identifying existing datasets, model artifacts, and saved preprocessing objects.
Generating a reusable project architecture and context document.
Maintaining project instructions and implementation decisions across development sessions.
Identifying missing components and duplicated code.
Understanding how changes to one pipeline stage affect downstream components.
What you'll build
- Use Google Antigravity or Claude Code to inspect an existing Python AI/ML project.
- Generate a project overview covering its architecture, datasets, dependencies, preprocessing pipeline, models, and execution steps.
- Ask AI to identify missing components and potential implementation issues.
- Reuse the generated context to implement a new feature without repeatedly explaining the project.
- Verify the generated project overview against the actual source code.
By the end: AI can work with a clearer understanding of the existing AI/ML codebase, reducing repeated analysis, unnecessary code changes, and time spent locating project components.
Accelerate the analysis of Software Requirements Specification (SRS) documents for AI/ML applications by extracting requirements, identifying data needs, and clarifying model-related expectations.
Topics covered
Extracting functional and non-functional requirements from AI/ML project SRS documents.
Identifying users, business workflows, and expected system outputs.
Extracting dataset requirements, input features, and target variables.
Identifying model prediction requirements and expected evaluation metrics.
Detecting ambiguous requirements and missing information.
Identifying data availability, quality, privacy, and access constraints.
Distinguishing traditional software functionality from AI/ML functionality.
Identifying model performance, latency, and interpretability requirements.
Generating structured requirement summaries and traceability tables.
What you'll build
- Process an SRS for a student performance prediction or customer churn prediction application.
- Use AI to extract functional requirements, non-functional requirements, data requirements, and model expectations.
- Generate a checklist of required datasets, input features, and expected predictions.
- Ask AI to identify unclear requirements, such as undefined prediction targets or missing evaluation criteria.
- Compare the AI-generated analysis against the original SRS and correct inaccurate interpretations.
By the end: AI reduces the manual effort required to analyze lengthy AI/ML requirements documents and helps developers identify missing data specifications, unclear model expectations, and implementation dependencies earlier.
Transform an AI/ML project's SRS into a structured Product Requirements Document (PRD) using AI, providing a clear foundation for implementation and validation.
Topics covered
Mapping SRS requirements to AI/ML product features.
Converting requirements into user stories and acceptance criteria.
Defining the scope of data processing, prediction, and reporting features.
Identifying data inputs, expected outputs, and user interactions.
Documenting model inference requirements and business workflows.
Defining model evaluation expectations and measurable success criteria.
Identifying dependencies between data pipelines, models, APIs, and interfaces.
Prioritizing features based on business value and implementation effort.
Generating requirement traceability tables.
Reviewing generated documentation for missing or unsupported requirements.
What you'll build
- Convert an SRS for an employee attrition prediction application into a PRD.
- Generate user stories for data upload, preprocessing, prediction, and result visualization.
- Define acceptance criteria for each major feature.
- Generate a requirements mapping table connecting SRS requirements to proposed product features.
- Ask AI to identify requirements that cannot be implemented until additional data or business decisions are provided.
- Validate the final PRD against the original SRS.
By the end: AI accelerates AI/ML product documentation and produces a structured, traceable implementation plan that connects business requirements with data-processing and model-related features.
Use AI to identify and organize the questions that must be answered before starting AI/ML development, reducing rework caused by unclear business objectives, incomplete datasets, and unrealistic model expectations.
Topics covered
Identifying ambiguous or incomplete AI/ML requirements.
Generating prioritized client clarification questions.
Clarifying the business problem and intended prediction or classification outcome.
Identifying data sources, data ownership, availability, and access permissions.
Clarifying target variables, input features, and historical data requirements.
Identifying missing values, data-quality concerns, and class imbalance.
Clarifying acceptable model performance and evaluation metrics.
Identifying privacy, security, fairness, and explainability requirements.
Clarifying prediction frequency, response time, and expected usage volume.
Identifying integration requirements and deployment constraints.
Converting client responses into updated requirements and acceptance criteria.
What you'll build
- Analyze a sample SRS for a customer churn prediction or hospital appointment forecasting application.
- Ask AI to identify missing information about the business objective, available data, target variable, and model evaluation.
- Generate a prioritized client questionnaire.
- Classify questions by business impact, data dependency, security, and technical feasibility.
- Use sample client answers to update the requirements and acceptance criteria.
- Review the updated requirements to ensure that no unsupported assumptions have been introduced.
By the end: AI reduces the time spent preparing requirement clarification questions and helps developers identify data limitations, business ambiguities, and model expectations before implementation begins.
Accelerate AI/ML system design by using AI coding assistants to propose architectures, identify component responsibilities, map data flows, and generate initial implementation plans.
Topics covered
Generating architecture proposals from AI/ML requirements.
Identifying components for data ingestion, preprocessing, feature engineering, training, evaluation, and inference.
Separating model-development code from application and API logic.
Designing reusable preprocessing and model pipelines.
Understanding batch inference versus real-time inference.
Designing database, file-storage, and model-artifact interactions.
Identifying API integration requirements.
Generating architecture diagrams and data-flow diagrams.
Comparing alternative designs based on complexity, maintainability, scalability, and cost.
Identifying error-handling, logging, security, and monitoring requirements.
Evaluating architecture proposals against project constraints.
Using AI to generate a phased implementation plan.
What you'll build
- Generate architecture proposals for a customer churn prediction application.
- Ask AI to identify components for dataset ingestion, preprocessing, model training, evaluation, and prediction.
- Compare a simple Python application with an API-based architecture where appropriate.
- Generate a component diagram and data-flow diagram.
- Ask AI to identify dependencies, possible failure points, and opportunities for reusable components.
- Generate a phased development plan with implementation and testing tasks.
- Validate the proposed architecture against the SRS and available infrastructure.
By the end: AI accelerates the preparation of AI/ML architecture proposals and supporting documentation, helping developers move from requirements to implementation with a clearer technical plan.
Accelerate dataset understanding and initial data-quality assessment through AI-assisted analysis.
Topics covered
Automated dataset inspection and summary generation.
Detection of missing values, duplicates, inconsistent formats, and unusual values.
Identification of numerical, categorical, and date-related columns.
Generation of descriptive statistics and visualizations.
Creation of data-quality reports and recommended checks.
What you'll build
- Use a customer dataset to generate an automated profiling report.
- Identify missing values, duplicates, outliers, and inconsistent categories.
- Produce a cleaned data-quality summary with the relevant Python code.
By the end: AI reduces manual exploratory data analysis and accelerates identification of dataset issues.
Reduce repetitive data preparation work through AI-assisted preprocessing code generation and validation.
Topics covered
Generating code for missing-value handling.
Detecting and correcting inconsistent data formats.
Encoding categorical variables and scaling numerical features.
Identifying duplicate records and potential data leakage.
Building reusable preprocessing pipelines.
Validating cleaned data against defined rules.
What you'll build
- Prepare a customer churn dataset containing missing values, duplicate records, and inconsistent categories.
- Generate a preprocessing pipeline using pandas and scikit-learn.
- Compare data-quality checks before and after preprocessing.
By the end: Faster preprocessing implementation with repeatable data-quality validation.
Accelerate the creation, testing, and refinement of useful machine learning features.
Topics covered
Generating candidate features from existing columns.
Creating date-based, numerical, categorical, and aggregated features.
Automating feature transformation code.
Evaluating feature usefulness through appropriate validation methods.
Identifying potential data leakage.
Documenting feature definitions and transformations.
What you'll build
- Use a sales dataset to generate features such as purchase frequency, average order value, and days since the last purchase.
- Implement the features in a reusable preprocessing pipeline.
- Compare model performance before and after feature engineering using the same validation strategy.
By the end: AI reduces repetitive feature-engineering coding and accelerates experimentation with candidate features.
Automate the generation of visualizations and analysis code that helps developers understand dataset patterns.
Topics covered
Generating charts using Matplotlib, Seaborn, and Plotly.
Creating correlation matrices and distribution plots.
Comparing target classes and feature distributions.
Identifying potential outliers and relationships.
Generating automated exploratory data analysis reports.
Producing concise summaries of observed patterns.
What you'll build
- Generate an exploratory analysis report for a loan approval dataset.
- Create visualizations for feature distributions, missing values, and target-class balance.
- Compare the generated report against the actual charts and underlying statistics.
By the end: Reduced time spent writing repetitive visualization code and assembling exploratory analysis reports.
Accelerate the selection of candidate machine learning algorithms based on the problem and dataset.
Topics covered
Identifying classification, regression, clustering, and anomaly-detection tasks.
Generating candidate algorithms for each problem type.
Comparing model complexity, interpretability, and computational requirements.
Selecting appropriate baseline models.
Defining evaluation metrics and validation strategies.
Comparing candidate models using measured results.
What you'll build
- Evaluate candidate models for a customer churn classification dataset.
- Generate baseline implementations using Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting.
- Compare their validation results using appropriate metrics.
By the end: AI accelerates initial model selection and baseline implementation, with final decisions based on empirical evaluation.
Automate the creation of repeatable machine learning training pipelines.
Topics covered
Generating training and validation workflows.
Integrating preprocessing and model training.
Creating reusable scikit-learn pipelines.
Automating model fitting and evaluation.
Saving trained models and training metadata.
Reproducing experiments using consistent configurations.
What you'll build
- Generate a training pipeline for a customer churn prediction model.
- Integrate preprocessing, model fitting, and validation.
- Run the pipeline and verify that the trained model and evaluation results are saved correctly.
By the end: AI reduces boilerplate code and accelerates the implementation of repeatable training workflows.
Reduce manual experimentation by automating hyperparameter search and comparison.
Topics covered
Generating parameter search spaces.
Configuring GridSearchCV and RandomizedSearchCV.
Using Optuna for more flexible optimization workflows.
Selecting suitable scoring metrics.
Tracking experiment configurations and results.
Balancing model quality, training time, and computational cost.
What you'll build
- Optimize a Random Forest classifier using scikit-learn.
- Compare baseline performance with hyperparameter-tuned performance.
- Record the selected parameters, evaluation metrics, and computation time.
By the end: Faster implementation of structured hyperparameter experiments without manually testing every parameter combination.
Accelerate the implementation and debugging of deep learning models.
Topics covered
Generating neural network architectures using PyTorch or TensorFlow/Keras.
Implementing forward passes, loss functions, and optimizers.
Configuring training and validation loops.
Implementing early stopping and checkpointing.
Investigating tensor-shape and training errors.
Evaluating model performance across training epochs.
What you'll build
- Implement a neural network for a classification task.
- Generate training, validation, and checkpointing code.
- Run the model, investigate training errors, and compare its validation performance with a baseline.
By the end: AI accelerates neural network implementation and reduces repetitive training-code development and debugging.
Accelerate natural language processing workflows through AI-assisted preprocessing, modeling, and evaluation code.
Topics covered
Text cleaning and normalization.
Tokenization and text representation.
TF-IDF and embedding-based approaches.
Classification and text similarity workflows.
Evaluation using appropriate NLP metrics.
Error analysis and documentation.
What you'll build
- Develop a sentiment analysis pipeline for customer reviews.
- Generate preprocessing and model-training code.
- Evaluate the model using precision, recall, F1-score, and a confusion matrix.
By the end: Reduced implementation time for common NLP preprocessing and modeling tasks.
Accelerate computer vision implementation using AI-assisted coding and model integration.
Topics covered
Image loading and preprocessing.
Image resizing, normalization, and augmentation.
Transfer learning with pretrained models.
Classification and object-detection workflows.
Model evaluation and visualization.
Troubleshooting image-shape and inference errors.
What you'll build
- Build an image classification pipeline using a suitable pretrained model.
- Generate preprocessing, inference, and evaluation code.
- Test the model on sample images and verify the predictions.
By the end: AI reduces repetitive computer vision implementation work and accelerates experimentation with pretrained models.
Accelerate the integration of large language models into Python applications.
Topics covered
Integrating hosted or locally deployed language models.
Generating API integration code.
Managing prompts and structured responses.
Handling timeouts, retries, and rate limits.
Validating generated outputs.
Measuring response quality, latency, and cost.
What you'll build
- Build a Python application that summarizes documents using an LLM.
- Implement response validation and error handling.
- Measure response latency and cost across a small test set.
By the end: AI-assisted development reduces the implementation effort for applications that use language models.
Accelerate the development of applications that answer questions using an external document collection.
Topics covered
Document ingestion and text extraction.
Text chunking and metadata handling.
Embedding generation.
Vector search and retrieval.
Context construction and answer generation.
Retrieval evaluation and citation verification.
What you'll build
- Build a document question-answering application using Python.
- Generate the document-processing, embedding, retrieval, and response-generation pipeline.
- Evaluate retrieval relevance and verify whether generated answers are supported by retrieved passages.
By the end: AI reduces the time required to assemble RAG components while enabling systematic evaluation of retrieval and answer quality.
Accelerate the selection of AI frameworks, models, and supporting infrastructure.
Topics covered
Comparing PyTorch, TensorFlow/Keras, and scikit-learn for suitable workloads.
Evaluating pretrained models and hosted model APIs.
Comparing embedding models and vector databases for retrieval tasks.
Assessing accuracy, latency, memory requirements, privacy, and cost.
Identifying when conventional software logic is preferable to an AI model.
Creating measurable model-selection criteria.
What you'll build
- Evaluate candidate frameworks and models for a document classification application.
- Compare at least two suitable candidates using a consistent evaluation dataset.
- Prepare a decision matrix covering quality, latency, resource requirements, and cost.
By the end: Faster technology evaluation and more evidence-based decisions about the AI components required for a project.
Automate test creation for preprocessing functions, model utilities, and machine learning components.
Topics covered
Generating pytest test cases.
Testing preprocessing and feature transformation functions.
Testing input validation and output shapes.
Verifying prediction interfaces and expected data types.
Testing edge cases and exceptional inputs.
Generating reusable test fixtures.
What you'll build
- Create unit tests for a machine learning preprocessing module.
- Include empty inputs, missing values, unexpected categories, and incorrect data types.
- Run the tests and correct any failures.
By the end: AI reduces the manual effort required to create unit tests for common ML components.
Accelerate verification of complete machine learning pipelines and dataset contracts.
Topics covered
Validating input schemas and expected columns.
Testing preprocessing and feature transformation consistency.
Detecting missing values and unexpected categories.
Verifying training and inference pipeline compatibility.
Testing prediction output schemas.
Generating data-quality and pipeline-validation reports.
What you'll build
- Build validation tests for a customer churn prediction pipeline.
- Introduce missing columns and unexpected input categories.
- Verify that invalid data is detected and that valid data produces predictions with the expected schema.
By the end: AI-assisted test generation reduces manual pipeline testing and helps detect data-related failures earlier.
Accelerate model evaluation and identify weaknesses through systematic analysis.
Topics covered
Generating evaluation code for classification and regression.
Selecting suitable metrics for the task.
Producing confusion matrices and error-analysis reports.
Examining false positives, false negatives, and large prediction errors.
Comparing models using consistent validation data.
Detecting potential data leakage and invalid evaluation procedures.
What you'll build
- Evaluate two candidate models on the same validation dataset.
- Generate metric summaries and visualizations.
- Analyze incorrect predictions and document the main performance limitations.
By the end: AI reduces evaluation-code development time and accelerates investigation of model errors without replacing statistical judgment.
Reduce the time required to diagnose training failures, inference errors, and performance bottlenecks.
Topics covered
Analyzing Python exceptions and training logs.
Investigating tensor dimensions and incompatible input formats.
Detecting preprocessing inconsistencies.
Profiling data loading and model inference.
Identifying memory and computation bottlenecks.
Verifying fixes through tests and performance measurements.
What you'll build
- Introduce a controlled shape mismatch or preprocessing inconsistency into an ML pipeline.
- Use an AI coding agent to identify the likely cause and implement a correction.
- Compare pipeline execution time and test results before and after the changes.
By the end: AI accelerates ML troubleshooting and targeted optimization while retaining evidence-based verification.
Automate the recording and comparison of machine learning experiments.
Topics covered
Tracking model parameters and evaluation metrics.
Recording dataset and preprocessing versions.
Logging experiment outputs.
Comparing model runs.
Integrating MLflow or an equivalent tracking tool.
Preserving reproducibility information.
What you'll build
- Track multiple model experiments using MLflow.
- Record hyperparameters, evaluation metrics, and model artifacts.
- Generate a comparison report showing the best-performing run and its configuration.
By the end: AI reduces manual experiment documentation and accelerates comparison of model-development iterations.
Accelerate the transition from a trained model to an application-ready inference service.
Topics covered
Packaging trained models and preprocessing components.
Creating inference functions.
Developing prediction APIs using FastAPI.
Implementing input validation and response schemas.
Managing model-loading errors and API exceptions.
Generating API tests and documentation.
What you'll build
- Package a trained classification model behind a FastAPI endpoint.
- Implement request validation and prediction responses.
- Test valid inputs, invalid inputs, and model-loading failures.
By the end: AI reduces the boilerplate required to expose trained models through testable Python APIs.
Accelerate model packaging, testing, and deployment preparation through repeatable MLOps workflows.
Topics covered
Creating reproducible training and inference environments.
Containerizing ML applications using Docker.
Automating tests and builds through CI/CD.
Managing model artifacts and versions.
Preparing deployment configuration.
Verifying deployed model endpoints.
What you'll build
- Containerize a FastAPI-based ML inference service.
- Configure a pipeline that runs tests and builds the container.
- Deploy to a test environment and verify predictions through the API.
By the end: AI reduces repetitive deployment preparation and helps standardize the transition from model development to serving.
Accelerate implementation of checks that detect changes in production data and model behavior.
Topics covered
Tracking input data quality.
Monitoring prediction distributions.
Detecting data drift and potential concept drift.
Measuring model performance when labels become available.
Generating alerts and monitoring summaries.
Triggering model review or retraining workflows when justified.
What you'll build
- Create a monitoring workflow for a deployed prediction model.
- Compare reference data with a new dataset containing shifted feature distributions.
- Generate a drift report and define thresholds that trigger further investigation.
By the end: AI accelerates the creation of monitoring code and reports that help identify changes requiring attention.
Automate the creation and maintenance of AI/ML project documentation.
Topics covered
Generating dataset and feature documentation.
Documenting preprocessing and training pipelines.
Recording model configurations and evaluation results.
Generating API and deployment instructions.
Summarizing experiments and model limitations.
Updating documentation when implementation changes.
What you'll build
- Generate a technical report for a completed classification project.
- Include dataset information, preprocessing steps, model configuration, metrics, and limitations.
- Update the report after changing the model and rerunning evaluation.
By the end: AI reduces repetitive documentation effort and improves the traceability of model-development decisions.
Combine AI-assisted data preparation, model development, testing, evaluation, and deployment into a repeatable workflow.
Topics covered
Establishing reusable master prompts and project context.
Automating dataset inspection and preprocessing.
Generating baseline models and training pipelines.
Implementing evaluation, testing, and error analysis.
Debugging and optimizing the pipeline.
Packaging and exposing the model through an API.
Preparing documentation and measuring development productivity.
What you'll build
- Develop a customer churn prediction application using Python, pandas, scikit-learn, pytest, and FastAPI.
- Use an AI coding agent to accelerate project analysis, preprocessing, training, testing, debugging, and API implementation.
- Evaluate model quality, verify test results, and run the inference service.
- Compare manual and AI-assisted development time, including verification and rework.
By the end: A repeatable AI-assisted ML development workflow that reduces repetitive coding and speeds up implementation while maintaining data quality, model evaluation, and software testing.
Live client projects — 5+ across the program
Across the Career-Ready program you work on all real client projects, contributing to each at a level appropriate to your current skills. By the final project, you're working at near-professional capacity.
Live client project
Context Assembly Framework (CAF)
Structured information collection, context normalization, template-based instruction assembly, version tracking, and feedback logging.
PythonStreamlitSQLiteJSON
Live client project
RecruitFlow Intelligence Engine
Bulk resume processing, candidate profiling, Gemini AI integration for resume understanding, MySQL-backed candidate matching and suitability scoring.
PythonGemini AIMySQLNLP
Live client project
SRS Vision AI — Smart Project Explainer and Visualizer
SRS document upload, section extraction, structured summaries, module identification, workflow visualization, and document-based search.
PythonDOCX processingMermaid.jsStreamlit
Live client project
SRSGen AI — Automated SRS Generator
Flask web app generating complete SRS documents from client input — with versioning, template mapping, DOCX export, and pytest test suite.
PythonFlaskSQLitepython-docxpytest
Live client project
TalentMatch AI — Candidate Matching Platform
Candidate skill profiling, weighted similarity scoring, automated candidate-to-project allocation, and ranking engine.
PythonScikit-learnPandasFastAPI
Live client project
Smart Task Priority Predictor
Historical issue analytics, priority classification model, deadline risk alerts, and backlog triage automation.
PythonXGBoostPandasREST APIs
Live client project
BugTrack Pro — Defect Management Platform
Defect reporting, severity classification, automatic assignee routing, SLA tracking, and resolution metrics dashboard.
PythonDjangoPostgreSQLDocker
Live client project
CodeReview AI — Automated Review System
Static analysis, style compliance verification, AST parsing, automated PR commenting, and defect prediction.
PythonASTLLM APIsGit Hooks
Everything included in your enrollment
✓
400+ live training hours — 5 hours daily, every weekday✓
100+ Immersive VR sessions + Unlimited VR revision for up to 2 full years (exclusive flagship privilege)✓
All 4 programs: internship + career course + industry projects + AI development✓
AI tools configured on your laptop (ChatGPT-4, Gemini, Copilot, Cursor — value ₹15,000+)✓
Internship + course certificate from WhiteandBox.com✓
Offer letter — verified, 0 background check issues✓
Letter of Recommendation (performance-based)✓
LinkedIn profile building, portfolio guidance, and soft skills training✓
Digital certificate with QR code verification✓
Free access to client summits and webinars (with certificate)✓
1:1 daily mentorship from senior developers✓
Remote, hybrid, or in-office at BangaloreCareer-Ready AI & Machine Learning Flagship Program
400+ live hours · 4 complete programs · 70+ modules · Full placement support. The program serious developers choose when they want job outcomes, not just a certificate.
₹38,000
₹17,850
50% off · limited seats
You save ₹20,150 at current offer — includes AI tools worth ₹15,000+ configured free on your laptop