🤖 Machine Learning Complete Course — Python

From Python Basics to Advanced ML Algorithms

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📌 Overview

A comprehensive collection of hands-on Machine Learning notebooks covering everything from Python fundamentals to advanced ML algorithms. Each module includes theory explanations, code walkthroughs, and real-world examples.

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📂 Course Modules

#	Module	Topics
01	Statistics II	Descriptive stats, distributions, hypothesis testing
02	Derivatives & Integration	Calculus for ML
03	Introduction to Python	Syntax, data types, control flow
04	Python Basics & Syntax	OOP, functions, modules
05	Lists, Dictionaries & Tuples	Data structures
06	If Conditions & Comparison	Logical operations
07	For Loops in Python	Iteration patterns
08	Functions & Objects	Reusable code design
09	NumPy	Arrays, vectorized operations
10	Pandas	DataFrames, data manipulation
11	Matplotlib & Seaborn	Data visualization
12	Supervised Learning	Regression & Classification
13	Unsupervised Learning	Clustering, PCA
14	Model Evaluation	Metrics, cross-validation
15	Feature Engineering	Encoding, scaling, selection

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🛠️ Tech Stack

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🚀 How to Use

git clone https://github.com/GhariebML/ML_Complete_Course_By_Python.git
cd ML_Complete_Course_By_Python
jupyter notebook

Or open any notebook directly in Google Colab.

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👨‍💻 Author

Mohamed Gharieb — Data Scientist & ML Engineer

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📄 License

MIT License — see LICENSE for details.

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⭐ Star this repo if it helped your ML journey!