ARTIFICIAL INTELLIGENCE COURSE - DURATION: 3 MONTHS (180 HOURS)

MODULE 1: Python Foundations for AI

• Getting Started with VS Code
• Creating Python Environments (virtualenv, Anaconda, etc.)
• Python Syntax and Semantics
• Variables and Data Types in Python
  • Lists, Tuples, Sets, Dictionaries
  • List Comprehension & Real-World Use Cases
• Operators (Arithmetic, Logical, Relational, etc.)
• Python Control Flow:
  • Conditional Statements (if, elif, else)
  • Loops (for, while, break, continue)

MODULE 2: Python Programming - Intermediate

• Functions in Python
  • Defining Functions
  • Lambda, map(), filter(), reduce()
• Built-in Python Modules: os, random, re, datetime
• File Handling (read, write, append modes)
• Exception Handling
• Object-Oriented Programming (OOP):
  • Class, Object, Inheritance
  • Polymorphism, Abstraction, Encapsulation

MODULE 3: Natural Language Processing (NLP)

• Introduction to NLP
• NLTK (Natural Language Toolkit)
• Tokenization, Stemming, Lemmatization
• Removing Stopwords, Named Entity Recognition (NER)
• Text Vectorization Techniques:
  • One-Hot Encoding (OHE)
  • Bag of Words (BoW)
  • N-Grams
  • TF-IDF
• Word Embeddings: Word2Vec
• Text Preprocessing (lowercasing, punctuation removal, etc.)
• Text Classification and Sentiment Analysis

MODULE 4: Deep Learning with Neural Networks

• Introduction to Deep Learning
• Understanding Neural Network Architecture
• Artificial Neural Networks (ANN)
  • Forward Propagation, Backpropagation
  • Cost Functions & Optimizers
• Recurrent Neural Networks (RNN) – Theory + Practical
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Bidirectional RNNs
• CNN Overview (for completeness)
• Hands-on Deep Learning with Keras/TensorFlow

MODULE 5: Attention Mechanism & Transformers

• Attention Mechanism Basics
• Encoder-Decoder Architecture
• Transformers Overview (BERT, GPT, T5)
• Positional Encoding
• Transfer Learning for NLP (fine-tuning BERT/GPT models)
• Pretrained Models with Hugging Face Transformers
• Text Summarization, Translation, and Q&A Applications

MODULE 6: Generative AI (Gen AI) & LLMs

• What is Generative AI?
• Large Language Models (LLMs) Introduction:
  • GPT, LLaMA, Claude, Mistral (brief overview)
• Components of LLMs:
  • Encoder, Decoder, Tokenizer, Attention Layers
• Model Evolution (GPT-1 → GPT-4 → GPT-4o)
• Tools & Platforms:
  • Ollama
  • Hugging Face
  • FAISS (Vector Store)
• RAG (Retrieval-Augmented Generation)
• Prompt Engineering
• Building Chatbots
• LangChain for LLM Application Frameworks