Скачать или смотреть Day-15/90 | AI, DS and ML complete course for beginners in Tamil | Hire Ready | Functions in Python

Day 15 of your Complete Artificial Intelligence (AI) Course focuses on one of the most powerful and essential concepts in Python programming: functions. Functions are reusable blocks of code that help you organize logic, avoid repetition, and build clean, modular programs – especially important in AI, Machine Learning, and Data Science where codebases quickly grow complex. In this session, you’ll learn what a function is, why functions are used, and how they make your AI code easier to manage, debug, and scale.

You’ll start by understanding the basic structure of a Python function: using the def keyword, writing a function name, defining parameters (arguments), adding a function body, and using the return statement to send values back to the caller. The lesson clearly explains the difference between defining a function and calling a function, so beginners can see how Python jumps in and out of function blocks during execution.

Next, you’ll explore different types of functions:

Functions with no parameters and no return value (used for printing messages or logging)

Functions with parameters but no return (used for performing actions based on input)

Functions that take parameters and return a value (used for calculations and transformations)

Functions that return multiple values using tuples (very useful in AI for returning metrics like accuracy and loss together)

A key topic in this session is variable scope – the difference between local variables (used inside a function) and global variables (defined outside). You’ll learn how scope affects data visibility and why it matters for avoiding bugs in larger AI projects. The lesson also introduces default arguments and keyword arguments, making your functions flexible and easier to use in different parts of your project.

Real-world examples show how functions are used in AI and data science workflows: writing a function to clean or preprocess data, a function to normalize values, a function to calculate metrics like accuracy, precision, or loss, and a function that wraps model prediction steps (taking input and returning an output prediction). These examples help you see how functions turn repeated logic into neat, reusable building blocks throughout your code.

You will also learn best practices for working with functions: choosing clear and descriptive function names, keeping functions short and focused on a single task, avoiding duplicate logic by reusing functions, and adding simple docstrings or comments when needed. This makes your code much easier to read, share, and extend – a critical skill for AI engineers, data scientists, and Python developers.

By the end of Day 15, you will have strong conceptual clarity about Python functions – including definitions, parameters, returns, scope, and usage in AI projects. You’ll be ready to write your own reusable function libraries, structure your AI code more professionally, and answer function-related questions in Python, Data Analyst, and AI Engineer interviews with confidence.