Machine Learning Fundamentals
Complete hands-on course with Python implementations and real-world examples
📚 Chapter 1: Introduction
What is Machine Learning?
- Types of machine learning
- Data preprocessing fundamentals
- Environment setup with Python
- Essential libraries (NumPy, Pandas, Scikit-learn)
Beginner
Python
Theory
📈 Chapter 2: Regression
Linear and Polynomial Regression
- Linear regression theory and implementation
- Polynomial regression and overfitting
- Multiple linear regression
- Feature importance analysis
Intermediate
Mathematics
Practical
🎯 Chapter 3: Classification
Logistic Regression and SVM
- Logistic regression for classification
- Support Vector Machines with kernels
- Model evaluation and comparison
- Hyperparameter tuning techniques
Advanced
Algorithms
Real-world
🧠Course Navigation
✅ Prerequisites:
• Basic Python programming
• High school mathematics (algebra, basic statistics)
• Curiosity about machine learning!