← Back to Training Overview

Choose Your Learning Track

🚀 Fast Track

For Experienced Professionals

  • Accelerated 16-week program
  • Assumes programming experience
  • Focus on GenAI concepts and applications
  • Quick dive into advanced topics
  • Ideal for developers, data scientists, ML engineers

📚 Regular Track

For Beginners & Detailed Learning

  • Comprehensive 24-week program
  • Starts from programming fundamentals
  • In-depth coverage of all concepts
  • More hands-on practice and projects
  • Perfect for career changers and beginners

Sample Week Schedule - Module 4

Machine Learning Fundamentals Week

Monday - Theory Session 1.5 hours

Supervised & Unsupervised Learning Concepts

  • Introduction to Scikit-Learn
  • Regression vs Classification
  • Clustering and Dimensionality Reduction
Wednesday - Hands-On Coding 1.5 hours

Model Evaluation & Cross-Validation

  • Train-Test Split implementation
  • Cross-validation techniques
  • Metrics: Accuracy, Precision, Recall, F1-Score
Friday - NLP Project 1.5 hours

Intro to NLP with TF-IDF + Logistic Regression

  • Text preprocessing and tokenization
  • TF-IDF vectorization
  • Building a text classifier
Saturday - Doubt Resolution 1 hour

Q&A Session & Concept Clarification

  • Review challenging concepts
  • Code debugging help
  • Assignment guidance

📝 Weekly Assignment: Build a complete sentiment analysis model using the techniques learned

Course Curriculum - Fasttrack

📘 Module 0: Introduction & Foundations

Duration: 2 sessions

  • Overview of the Generative AI landscape
  • Traditional ML vs Deep Learning vs GenAI
  • Use Cases: Chatbots, Copilots, Code Gen, etc.
  • Real-world Job Expectations & Interview Prep
  • Environment Setup: Mac/Windows, VS Code, MySQL

🐍 Module 1: Programming & Data Foundations

Duration: 2 weeks

  • Python Essentials
  • Inbuilt Libraries for GenAI Projects
  • Data Wrangling: Pandas & NumPy
  • Visualization: Matplotlib, Seaborn, Plotly

🧠 Module 2: Generative AI Concepts

Duration: 2 weeks

  • LLMs: GPT, LLaMA, Claude, etc.
  • Transformer Architecture
  • Tokenization, Embeddings, Decoding
  • Language Modeling & Text Generation

✍️ Module 3: Prompt Engineering & Responsible AI

Duration: 2 weeks

  • Prompt Types: Templates, Few-Shot, CoT
  • LangChain / LlamaIndex for Chaining
  • Mitigating Hallucinations
  • Ethical AI: Bias, Safety, Attribution

📊 Module 4: Machine Learning Fundamentals

Duration: 1 week

  • Supervised & Unsupervised Learning (Scikit-Learn)
  • Model Evaluation & Cross-Validation
  • Intro to NLP: TF-IDF + Logistic Regression

🔬 Module 5: Deep Learning with PyTorch

Duration: 2 weeks

  • Neural Networks, Loss, Optimizers
  • CNNs, RNNs, LSTMs
  • Attention Mechanism & Transformers
  • Transfer Learning & Fine-Tuning
  • Integrating ML in GenAI Pipelines

🛠️ Module 6: LLM Application Development

Duration: 3 weeks

  • OpenAI APIs: Chat, Embeddings, Moderation
  • Azure OpenAI & Fine-Tuning Basics
  • Vector DBs: Pinecone, ChromaDB, Weaviate
  • RAG Pipelines, ReAct, Assistants API
  • Streamlit / Gradio Prototyping

☁️ Module 7: Linux & Cloud Setup

Duration: 1 week

  • Linux Basics
  • Cloud Setup: Azure & Firebase
  • Cloud Storage & Databases
  • Cloud Functions & ML on Cloud

🚀 Module 8: Deployment & MLOps

Duration: 1 week

  • Docker for ML/AI
  • Model Lifecycle Management
  • GenAI Deployment on Cloud
  • Logging, Monitoring, Observability
  • CI/CD with GitHub Actions
  • Mini Project: End-to-End GenAI App

🏁 Module 9: Capstone Project

Duration: 2 weeks

  • Build a complete LLM-based App
  • Examples: Chatbot, Tutor, Support Bot
  • Includes Prompting, Vector DB, OpenAI API & UI

🎁 Add-On Modules (Post-Course)

Free extensions based on market needs

  • LangGraph, AutoGPT, OpenAgents
  • MCP Server Development
  • Multimodal GenAI
  • DSPy (Declarative Prompting)
  • LangChain Advanced
  • Fast UI Prototyping with NiceGUI