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🧭 Python Learning Roadmap (Beginner to Deep Learning)

Welcome to your personal Python mastery journey 🐍!
Click on each topic to explore its dedicated page (make sure to create those .md files in your wiki with matching titles).

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🟢 Level 1: Core Python (Beginner to Intermediate)

✅ Topics:

• Python Setup & IDEs

• Variables

• Data Types

• Input and Output

• Control Flow: if Statements

• Control Flow: for Loops

• Control Flow: while Loops

• Functions: Basics

• Functions: Parameters and Return Values

• Scope and Global Variables

• Lists

• Tuples

• Sets

• Dictionaries

• List Comprehensions

• Set and Dict Comprehensions

• String Formatting

• String Methods

• Determining the Types of Data Objects

• Error Handling with try/except

• Raising Exceptions

• Modules & Importing

• Using pip

• Reading Files

• Writing Files

• Classes and Objects

• Inheritance in OOP

• Virtual Environments with venv

• Conda Environments

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🔵 Level 2: Popular Standard Libraries & Tools

✅ Topics:

• NumPy: Arrays

• NumPy: Broadcasting

• NumPy: Indexing and Slicing

• Pandas: DataFrames

• Pandas: CSV & Excel

• Pandas: Data Cleaning

• Pivot Tables with pandas

• Lifelines Library

• Math

• Matplotlib Basics

• Seaborn Basics

• Regex Basics

• Regex Patterns

• Datetime Module

• Working with Dates

• OS Module

• Sys Module

• Python Logging

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🟡 Level 3: Advanced Python Concepts

✅ Topics:

• Decorators

• Generators

• Lambda Functions

• Higher-Order Functions

• Iterators

• Context Managers

• Multithreading

• Multiprocessing

• Type Hinting

• Function Annotations

• Unit Testing with unittest

• Unit Testing with pytest

• Profiling with timeit

• Profiling with cProfile

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🔴 Level 4: Machine Learning & Deep Learning Frameworks

⚙️ Scientific & ML Foundations

• Scikit-learn Overview

• Statsmodels Overview

🔬 Deep Learning Frameworks

• Keras Overview

• TensorFlow Overview

• PyTorch Overview

• OpenCV for Vision

• Hugging Face Transformers

• FastAI Overview

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🧠 Deep Learning Concepts

• Tensors & Gradients

• Activation Functions

• Loss Functions

• Optimizers (SGD, Adam)

• Backpropagation

• CNNs (Convolutional Neural Networks)

• RNNs & LSTMs

• Transformers & Attention

• Training vs Validation

• Overfitting & Regularization

• GPU Acceleration with CUDA