Python Basics for Machine Learning

Python is the most widely used language for machine learning, mainly because of its simple syntax and the huge number of libraries built specifically for data and AI work.

Variables and Data Types

Python lets you store data in variables without explicitly declaring their type.

age = 25

name = "Claude"

price = 99.99

is_valid = True

Lists and Dictionaries

Lists store multiple values in order, while dictionaries store data as key-value pairs. Both are used heavily when handling datasets.

fruits = ["apple", "banana", "mango"]

student = {"name": "Alex", "score": 85}

Loops

Loops let you repeat actions, which is useful when processing rows of data.

for fruit in fruits:

    print(fruit)

Functions

Functions let you group reusable blocks of code, keeping machine learning scripts organised.

def calculate_average(numbers):

    return sum(numbers) / len(numbers)



print(calculate_average([10, 20, 30]))

Conditional Statements

Conditions let your program make decisions based on data values.

score = 75



if score >= 50:

    print("Pass")

else:

    print("Fail")

These basics form the foundation for every machine learning task, since libraries like NumPy, Pandas, and Scikit-learn are all built using core Python concepts like these.