AI/ML & Generative AI Foundation Program
Machine Learning • Deep Learning • LLMs • Vector DB • RAG • Agentic AI
A practical, hands-on 12-week program covering Machine Learning foundations, Deep Learning fundamentals, Generative AI, LLM application development, embeddings, vector databases, RAG, industry project lifecycle and production engineering.
What You Will Master in 12 Weeks
A practical, hands-on 12-week program covering Machine Learning foundations, Deep Learning fundamentals, Generative AI, LLM application development, embeddings, vector databases, RAG, industry project lifecycle and production engineering.
Program Highlights
Daily & Weekly Learning Routine
- Daily 90-minute structured classes with live interactive demonstrations
- Daily oral assessments and coding challenges
- Weekly twice Show & Tell architecture sessions
- Weekly doubt-clearing and personalized mentoring
- Monthly mini projects and final capstone presentation
Technologies & Core Competencies
Targeted industry stack covered through live coding, architecture blueprints, and dedicated sandbox labs.
Core Competencies Covered
Enterprise Tools & Platforms
Comprehensive 12-Week Curriculum
Exact, comprehensive weekly topics taken directly from official CES program specifications. Every week features hands-on assignments and code reviews.
Python for AI/ML
Foundational programming in modern Python with scientific data libraries and version control
- Python fundamentals
- Functions
- Object-oriented programming basics
- Exception handling
- File handling
- Modules/packages
- +7 more topics
Sales Data Analysis (Load, clean, transform, analyze, visualize and push to GitHub)
Data Preparation & Statistics
Statistical data profiling, cleaning anomaly data, and professional ML feature engineering
- Data types
- Data profiling
- Missing values
- Duplicate data
- Outliers
- Correlation
- +13 more topics
Customer Churn Data Preparation Pipeline
Machine Learning Fundamentals
Understanding supervised vs unsupervised paradigms and algorithm selection criteria
- AI vs ML vs Deep Learning
- Supervised learning
- Unsupervised learning
- Regression
- Classification
- Model training
- +9 more topics
Customer Churn Prediction (Model training & performance baseline)
ML Model Evaluation & Hyperparameter Tuning
Rigorous statistical validation metrics and automated optimization methods
- Confusion matrix
- Accuracy
- Precision
- Recall
- F1-Score
- ROC-AUC
- +10 more topics
Loan Approval / Shopfloor Prediction (Model comparison & selection)
Unsupervised Learning & Clustering
Discovering hidden groupings, customer segmentation, and dimensionality reduction
- Clustering
- K-Means algorithm
- Hierarchical clustering
- PCA (Principal Component Analysis)
- Dimensionality reduction
- Customer segmentation techniques
Customer Segmentation (Clustering purchasing behavior into actionable profiles)
End-to-End ML Application
Transitioning from notebook experimentation to production-ready inference scripts
- Combining data pipelines with trained models
- Model persistence (Pickle/Joblib)
- Inference pipelines
- Handling raw inputs
- Edge case validations
Employee Attrition Prediction System
Deep Learning Fundamentals
Conceptual understanding of neural network mechanics and practical PyTorch workflows
- Neural networks
- Neurons
- Weights & Bias
- Activation functions
- Forward propagation
- Backpropagation
- +9 more topics
PyTorch Image Classification Mini Project
Generative AI & LLM Fundamentals
Transformer architectures, tokenization, foundation models, and provider APIs
- AI vs ML vs GenAI
- Generative models
- Foundation models
- LLMs
- Tokens & Tokenization
- Context window
- +5 more topics
LLM Text Assistant (API integration & conversational handler)
Prompt Engineering & LLM Applications
Enterprise LLM application design patterns beyond simple text prompting
- Prompt structure
- Zero-shot prompting
- Few-shot prompting
- Role prompting
- Context engineering
- Structured outputs
- +5 more topics
Resume Analyzer, Document Summarizer, and SQL Assistant apps
Embeddings & Vector Database Fundamentals
High-dimensional semantic representation, distance metrics, and vector storage engines
- What is an embedding?
- Text to vector mapping
- Semantic similarity
- Cosine similarity
- Vector search
- Top-K retrieval
- +4 more topics
Semantic Search Application (Indexing and query matching)
Retrieval-Augmented Generation (RAG) Fundamentals
Building enterprise knowledge grounding pipelines to eliminate hallucinations
- What is RAG?
- Why RAG?
- Hallucination mitigation
- RAG architecture
- Document loaders
- Chunking strategies
- +5 more topics
Document Ingestion & RAG Question Answering Pipeline
Foundation Capstone & 6-Phase SDLC Defense
Building and defending a complete, production-packaged AI Document Assistant
- FastAPI microservices
- Docker containerization
- Unit testing with Pytest
- Logging and basic monitoring
- 6-Phase SDLC (Requirements, Design, Code, Test, Deploy, Document)
- Architecture presentation and defense
AI-Powered Document Assistant Capstone (PDF upload, chunking, vector storage, QA, citations)
Industry Projects & Capstone Deliverables
Graduation requires building and presenting an enterprise portfolio project following the standard software development lifecycle.
AI-Powered Document Assistant (Capstone)
An enterprise knowledge assistant that processes multi-page corporate PDFs, chunks text semantically, generates vector embeddings, stores them in ChromaDB, and performs cited question-answering via FastAPI microservices.
Customer Churn Data & Prediction Pipeline
End-to-end data cleaning, outlier imputation, feature scaling, and multi-model benchmarking comparing Decision Trees, Random Forests, and Logistic Regression with GridSearchCV tuning.
Semantic Search & Corporate Knowledge Engine
A vector-indexed retrieval system supporting natural language queries across product documentation with top-K similarity scoring and metadata filtering.
Who Can Join This Program
- B.Tech / M.Tech / MCA / B.Sc graduates in CS, IT, ECE, Data Science or related technical disciplines
- Software developers aiming to transition into modern Generative AI and LLM application development
- Data analysts and programmers with basic Python fundamentals seeking production-grade AI portfolio projects
Laptop & System Requirements
- Basic familiarity with programming concepts (Python or C/Java)
- Basic math and statistics interest
- Broadband internet for live labs
Target Career Opportunities & Preparation
Target Job Roles
Interview Preparation Structure
- Weekly technical mock interviews on ML concepts, algorithms, and LLM mechanics
- Architecture defense practice: explaining vector search, cosine distance, and RAG pipelines
- Resume architecture and GitHub project portfolio showcase review
- Live coding assessments and problem-solving interview rounds
Questions About This Program
Do I need a strong mathematical background before joining?
No advanced mathematics degree is required. We cover the necessary linear algebra and statistics fundamentals (distributions, vectors, cosine distance) from the ground up during Weeks 1 and 2.
Will we build real projects or just follow tutorials?
Every student builds independent weekly projects, monthly mini-projects, and a major capstone (AI Document Assistant) following the 6-phase industry SDLC with code pushed to GitHub.
Is this training live or pre-recorded?
All sessions are live, interactive 90-minute daily classes led by practicing industry practitioners, with dedicated lab sessions and weekly oral reviews.
Ready to Begin the AI/ML & Generative AI Foundation Program?
Register your profile for skill baseline evaluation or speak directly with an academic advisor via WhatsApp to confirm the next batch schedule.