Generative AI Course – A power-packed collection of nearly 100 hours of immersive AI learning, designed to take you from beginner to expert with practical projects, live guidance, and real industry workflows — with absolutely no prior AI knowledge required.

•  100 hours of Recorded Content
•  5 End-to-End Project
•  No Prior Knowledge Required
•  Notes and code for every topic included.
•  Industry-Level Case Studies 
•  Certificate of Completion
•  Doubt Resolution Team

Comprehensive Roadmap For Deep Learning and GEN-AI

Basic Mathematics for Deep Learning
1. Linear Algebra (Matrices, Vectors, Eigenvalues)
2. Probability & Statistics (Bayes Theorem, Gaussian Distribution)
3. Calculus (Derivatives, Partial Derivatives, Chain Rule)

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Fundamentals of Neural Networks & Deep Learning
1. What is a Neural Network?
2. Artificial Neural Networks (ANN/MLP)
3. Backpropagation & Gradient Descent
4. Dropout & Batch Normalization
5. Optimization Techniques (Adam, RMSProp, SGD)

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Natural Language Processing (NLP)
1. NLP Basics & Text Preprocessing
2. Types of Embeddings:
  2.1 Word2Vec (CBOW, Skip-gram)
  2.2 TF-IDF, Bag of Words (BoW), N-grams
3. Recurrent Neural Networks (RNN) & Their Limitations
4. Long Short-Term Memory (LSTM)  

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Transformer Models & Foundation Models
1. Encoder-Decoder Architecture
2. "Attention Is All You Need" Paper (The Birth of Transformers)
3. Transformer Architecture:
  3.1 Self-Attention & Multi-Head Attention
  3.2 Positional Encoding & Feedforward Layers
4. Project On Transformer

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Generative AI – LangChain

1. Models in LangChain (OpenAI, Gemini, Claude)

2. Prompts in LangChain

3. Structured Output in LangChain
    3.1 Pydantic / Output Parsers
    3.2 Enforcing Schema-Based Responses

4. Vector Databases
     4.1 Embeddings
     4.2 Vector Stores (FAISS, Chroma, Pinecone)
     4.3 Similarity Search & Retrieval

5. RAG (Retrieval Augmented Generation)
    5.1 Building a Basic RAG Pipeline
    5.2 Query Transformation & Retrieval
    5.3 Response Generation

6. YouTube Case Study – RAG (End-to-End Pipeline)

7. Agents in Gen-AI
    7.1 Tools, Actions & Executors
    7.2 ReAct Framework
    7.3 Building a Multi-Tool Agent

8. Evaluations of LLM
     8.1 RAG Evaluation Metrics
     8.2 Agent Evaluation
     8.3 Scoring & Benchmarks

9. Agentic Project (Final Project)
    9.1 Build an Agentic Workflow
    9.2 Integrate External Tools & Vector DB
    9.3 Deploy the Agent as a Complete Gen-AI Application