🤖 Machine Learning Practice

This repository contains my hands-on implementation of Machine Learning algorithms and concepts from the "Complete Machine Learning & Data Science Bootcamp" by Krish Naik on Udemy.

📚 Learning Objectives

• Master Exploratory Data Analysis (EDA) and Feature Engineering.
• Implement Supervised Learning (Regression, Classification) and Unsupervised Learning (Clustering).
• Understand the mathematical intuition behind algorithms like Random Forest, XGBoost, and SVM.
• Deep dive into Hyperparameter Tuning and Model Deployment.

[Image of machine learning model training workflow]

📂 Repository Structure

• Notebooks/: Jupyter notebooks with step-by-step code and comments.
• Scripts/: Modular Python scripts for reusable ML components.
• Models/: Saved serialized models (.pkl or .h5).

📊 Data Source

To keep this repository lightweight, all datasets used in these practices are stored in my dedicated data repository: 🔗 Machine Learning Data

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🔗 Connect & Portfolio

• Portfolio: Visit My Website
• LinkedIn: Connect with Muhammad Noman

Developed by Noman BSCS @ UET Taxila | IBM Certified Data Scientist