NumPy Essentials for ML

NumPy (Numerical Python) is the foundation of the Python data science stack. Every ML library - including scikit-learn, TensorFlow, and PyTorch - is built on NumPy.

Why NumPy for Machine Learning?

Creating Arrays for ML

Array Shape and Reshaping

Understanding array shapes is crucial for ML - models expect specific shapes.

Indexing and Slicing

Selecting parts of your data is essential for ML.

Mathematical Operations for ML

Practice: Feature Scaling

Practice: Calculate Mean Squared Error

Key NumPy Functions for ML

Key Takeaways

• NumPy arrays are faster and more efficient than Python lists
• Shape is crucial: (samples, features) for ML data
• Use reshape to format data correctly for models
• Slicing helps with train/test splits
• NumPy provides all the math operations ML needs
• All ML libraries expect NumPy arrays as input

Next, we'll learn Pandas for handling real-world datasets!