📍 Phase 12: Deep Learning (Week 18–19)

• Neural Networks

• Perceptron

• Activation Functions

• Backpropagation

• TensorFlow

• Keras

• PyTorch

• CNN Basics

• RNN Basics

• LSTM Basics

📍 Phase 13: Generative AI & LLMs (Week 20)

• Transformers

• Attention Mechanism

• Large Language Models (LLMs)

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• Embeddings

• Vector Databases

• AI Agents

• LangChain

• LlamaIndex

📍 Phase 14: Model Deployment (Week 21)

• Flask

• FastAPI

• Streamlit

• Docker Basics

• REST APIs

• Model Serialization (Pickle, Joblib)

📍 Phase 15: MLOps (Week 22)

• ML Pipelines

• Model Versioning

• Experiment Tracking (MLflow)

• CI/CD for ML

• Model Monitoring

• Data Drift

• Model Retraining

📍 Phase 16: Cloud for Data Science (Week 23)

• AWS Basics

• Amazon S3

• Amazon SageMaker

• Azure ML

• Google Vertex AI

• Databricks Basics

📍 Phase 17: Git & GitHub (Week 24)

• Git Basics

• Branching

• Merging

• Pull Requests

• GitHub Portfolio

📍 Phase 18: Data Science Projects (Week 25–26)

Build at least 10 end-to-end projects, such as:

• House Price Prediction

• Customer Churn Prediction

• Credit Card Fraud Detection

• Loan Approval Prediction

• Sales Forecasting

• Movie Recommendation System

• Sentiment Analysis

• Employee Attrition Prediction

• Image Classification

• End-to-End RAG Chatbot

📍 Phase 19: Portfolio Building

• GitHub Profile

• Project Documentation

• Technical Blog Writing

• Resume Optimization

• LinkedIn Optimization

• Kaggle Profile

📍 Phase 20: Interview Preparation

• Python Interview Questions

• SQL Interview Questions

• Statistics Questions

• Machine Learning Questions

• Case Studies

• Coding Round

• Business Problem Solving

• Mock Interviews

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