🧠 PHASE 1: Python Core (Foundation Layer)

🎯 Goal: Build a solid foundation — just like Java fundamentals before Spring Boot.

🔹 1. Python Basics

• Installation (Windows/Linux/macOS)

• pip, venv, IDEs (VS Code / PyCharm / IntelliJ)

• Python Script Execution, __main__

• Comments, Syntax Rules

✅ Topics

• Data Types: int, float, str, bool, None

• Type Casting, type(), isinstance()

• Operators: Arithmetic, Logical, Bitwise, Comparison

• f-Strings, String methods (split, strip, etc.)

🔹 2. Control Flow

• if, elif, else

• for and while loops

• Loop control: break, continue, pass

• Comprehensions: List, Dict, Set

🔹 3. Functions

• Defining/Calling Functions

• Arguments: positional, keyword, default

• *args and **kwargs

• Lambda Functions

• Recursion

🔹 4. Data Structures

• Lists, Tuples, Sets, Dictionaries

• Nested structures

• Iterating & Modifying collections

• Built-in functions: map(), filter(), zip(), enumerate()

• collections module: Counter, defaultdict, deque

🔹 5. Object-Oriented Programming

• Classes & Objects

• __init__, self, Class Attributes

• Inheritance, Method Overriding

• Polymorphism, Encapsulation

• Dunder methods: __str__, __repr__, __eq__, etc.

• @classmethod, @staticmethod, @property

🔹 6. Error Handling

• try, except, finally, else

• Custom Exceptions

• raise, assert

🔹 7. Modules & Packages

• Creating and importing modules

• __name__ == "__main__"

• Built-in modules: math, os, sys, random, datetime

• Installing external packages (pip)

🔹 8. File I/O

• Reading and writing .txt, .csv, .json

• Context managers: with open()

• JSON parsing: json.load, json.dump

⚙️ PHASE 2: Python REST API Development

🎯 Goal: Build Spring Boot–like REST APIs using Flask and FastAPI

🔸 Step 1: Flask (Foundation)

• Project structure

• HTTP Methods: GET, POST, PUT, DELETE

• request.args, request.form, request.json

• Return JSON responses

• Templates with Jinja2 (optional)

• Blueprints (modular architecture)

• Static files, Middleware

✅ Project:

• Mini Blog API

• User Signup/Login

🔸 Step 2: FastAPI (Spring Boot Level)

✅ Basics

• @app.get, @app.post, @app.put, @app.delete

• Path & Query Parameters

• Swagger UI, Redoc auto-docs

• JSON response + ResponseModel

• Validation with Pydantic

✅ Intermediate

• Dependency Injection

• Custom Exceptions + HTTP Status Codes

• Background Tasks

• Middleware

• CORS Handling

✅ JWT Authentication

• Password hashing (passlib)

• OAuth2 token flow

• JWT encode/decode

• Refresh tokens

🔸 Step 3: Database Layer

✅ SQLAlchemy ORM

• Models, Columns, Relationships

• One-to-One, One-to-Many

• CRUD with DB sessions

• Async support with SQLAlchemy 2

✅ Alembic

• Auto migrations

• Versioning DB schema

🔸 Step 4: Advanced Features

• Pagination

• Sorting, Filtering

• File upload/download

• Logging

• Environment variables with dotenv

• Email notifications (smtplib, FastAPI BackgroundTasks)

• Redis-based Caching

• Testing with pytest

🔸 Step 5: Production Readiness

• Dockerizing API (Dockerfile, docker-compose)

• Reverse proxy with Nginx

• Deploy on:

  • Railway

  • Render

  • AWS EC2 / Lightsail

  • VPS (manual deployment)

• GitHub Actions for CI/CD

🤖 PHASE 3: Python AI / ML / ChatGPT & Agents

🎯 Goal: Build LLMs, intelligent tools, and deploy ChatGPT-like projects.

🔹 Step 1: Data Science

✅ Libraries

• NumPy (vectors, matrices)

• Pandas (DataFrame, ETL)

• Matplotlib, Seaborn (Visualization)

• Sklearn (Pipelines, metrics)

🔹 Step 2: Machine Learning

✅ Concepts

• Supervised: Regression, Classification

• Unsupervised: Clustering, Dimensionality Reduction

• Model Evaluation: confusion matrix, F1 score, ROC

• Cross-validation, GridSearchCV

• Save/load model: joblib, pickle

🔹 Step 3: Deep Learning (PyTorch / TensorFlow)

• Neural Networks (MLP, CNN, RNN)

• Activation Functions: ReLU, Sigmoid, Tanh

• Backpropagation & Loss Functions

• Optimizers: SGD, Adam

• Dropout, BatchNorm

🔹 Step 4: Natural Language Processing

• Tokenization, Stopwords

• Word Embeddings: Word2Vec, GloVe

• BERT, GPT, LLaMA models

• HuggingFace transformers usage

• Sentence Transformers + Vector Search (FAISS)

🔹 Step 5: LLM Agents & AI Tools

✅ Libraries:

• LangChain

• Autogen / CrewAI

• ChromaDB / FAISS / Weaviate

• Whisper (Speech-to-Text)

• ElevenLabs / TTS

• OpenAI API

✅ Projects:

• AI Voice Assistant (LangChain + Whisper + TTS)

• GPT API Wrapper with Memory & Search

• PDF & Document Q&A Bot

• Custom Knowledge Bot with Vector DB

• AI + FastAPI Backend Integration

🧩 Final Tools & Technologies Summary