Python Basics

Python Basics

This section covers essential Python programming concepts for machine learning (ML) tasks. You’ll learn syntax, data types, and libraries, preparing for ML labs with numpy, pandas, and scikit-learn. Basic familiarity with Python helps, but no ML experience is required.

Why Python for ML?

Python dominates AI/ML because of its simplicity, readability, and massive ecosystem. Its high-level syntax enables rapid prototyping, while libraries like numpy, pandas, and pytorch handle performance-critical operations in optimized C/C++ under the hood.

Basic Syntax

Python’s syntax is simple and beginner-friendly. This program sums a list, showing variables, loops, and functions:

def main():
    numbers = [1, 2, 3, 4, 5]
    total = 0
    for num in numbers:
        total += num
    print("Sum:", total)

if __name__ == "__main__":
    main()

• Variables: Declared dynamically, no type annotations needed (total = 0).
• Lists: [] stores ordered data, commonly used for datasets.
• Loops: for iterates directly over items.
• Functions: def defines functions, with main() as convention.

Run with python main.py to see “Sum: 15”.

Data Structures for ML

ML tasks require arrays, matrices, and datasets. Python provides:

• Lists (list): Flexible collections for data.
• Tuples (tuple): Immutable sequences.
• Dictionaries (dict): Key-value pairs for metadata.
• NumPy Arrays: Efficient multidimensional arrays (preferred for ML).

Example dataset with Python structures:

dataset = {
    "features": [[1.0, 2.0], [3.0, 4.0]],
    "labels": [0, 1]
}

print("Dataset size:", len(dataset["features"]))

This mimics ML datasets, later used with numpy, pandas, or scikit-learn.

Lab: Vector Operations with NumPy

Practice Python by computing the Euclidean distance between two vectors, a key ML operation.

1. Create vector_distance.py in your project:

   import numpy as np
   
   def euclidean_distance(v1, v2):
       v1, v2 = np.array(v1), np.array(v2)
       if v1.shape != v2.shape:
           raise ValueError("Vectors must have equal length")
       return np.sqrt(np.sum((v1 - v2) ** 2))
   
   if __name__ == "__main__":
       v1 = [1.0, 2.0, 3.0]
       v2 = [4.0, 5.0, 6.0]
       distance = euclidean_distance(v1, v2)
       print(f"Euclidean Distance: {distance:.3f}")

2. Dependencies: Install NumPy if not already installed:

   pip install numpy

3. Run:

   python vector_distance.py

   Expected Output:

   Euclidean Distance: 5.196

This builds skills for ML computations, using NumPy for efficient vector operations.

Learning from Official Resources

Deepen your Python knowledge with:

• Python Official Tutorial: docs.python.org/tutorial
• Automate the Boring Stuff with Python by Al Sweigart: Beginner-friendly, practical examples.
• Effective Python by Brett Slatkin: Best practices for writing clean, efficient code.

Next Steps

Move to First ML Lab to build your first ML model, or revisit Setup.

Further Reading

• Python Documentation: docs.python.org
• Automate the Boring Stuff with Python by Al Sweigart
• Effective Python by Brett Slatkin