Python for AI & ML

In this course we will be learning the most important Python concepts you need to get started with AI and ML. You do not need to be an expert Python programmer to start. You need to understand arrays, loops, functions, and how to use libraries. Everything beyond that you will pick up as you go.

Why Python for AI and ML

Python has become the standard language for machine learning and data science. Its readable syntax, massive ecosystem of libraries, and active community make it the fastest path from idea to working model. Every major ML framework — TensorFlow, PyTorch, scikit-learn — is built for Python first.

Setting Up Your Environment

Here is what you need to get started:

• Python 3.10+ — the language runtime
• pip — package installer; use a virtual environment or Anaconda for dependency management
• Jupyter Notebooks — the primary tool for experimentation; covered in the Python Libraries section
• NumPy, scikit-learn, Matplotlib — the key packages you will use throughout this track

Install core ML packages:

pip install numpy scikit-learn matplotlib jupyter

Python Concepts You Will Use Most

ML code uses a small, focused subset of Python. Here are the concepts you will use most:

• lists and loops — for basic data handling
• functions — to encapsulate operations
• classes — occasionally for custom models
• NumPy arrays — the most important concept; every matrix, every dataset, every set of predictions is stored as an array

Variables and basic types:

learning_rate = 0.01       # float
num_iterations = 1000      # int
feature_names = ['age', 'income', 'score']  # list

A simple function:

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

np.exp here comes from NumPy. We will take a detailed look at NumPy and its methods in the NumPy Fundamentals module later in this track.

Indentation and Structure

Python uses indentation (4 spaces) to define code blocks — there are no curly braces. Getting the indentation wrong is the most common Python error for beginners. Every loop, function, and class body must be consistently indented.

Correct indentation:

for i in range(10):
    print(i)          # inside the loop
    if i > 5:
        print("big")  # inside the if

This would raise an IndentationError:

for i in range(10):
print(i)  # missing indentation

Data Types That Matter in ML

You will work with three Python data types constantly.

1. floats — decimal numbers like weights and probabilities
2. integers — counts like number of examples
3. booleans — True/False for labels in classification

Everything else — images, text, audio — gets converted to arrays of these types before the model sees it.