Learn Python Free — Tutorials & Practice for Students

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TL;DR: Free Python tutorials, examples and coding practice for Indian students. Beginner to advanced, with an in-browser editor and AI feedback — no cost.

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Free Python course: 50 lessons, from the basics through to the advanced topics interviewers actually ask about. Every lesson is free, needs no sign-up, and runs in your browser with an editor and instant AI feedback.

Python Course Syllabus — 50 Lessons

Work through them in order, or jump to the topic you need. 49 lessons include worked explanations and runnable examples.

Getting Started

• Introduction to Python — Python is a high-level, interpreted programming language designed to be simple, clean, and readable.
• Variables & Data Types — A variable is a named container that stores a value in memory.
• Strings & String Methods — A string is a sequence of characters enclosed in single ('...') or double ("...") quotes.

Data Structures

• Lists — A list is an ordered, mutable (changeable) collection of items.
• Dictionaries — A dictionary stores data as key-value pairs, like a real dictionary where you look up a word (key) to find its meaning (value).

Control Flow

• If / Elif / Else — Conditional statements let your program make decisions.
• For & While Loops — Loops let you repeat code. Python has two types: for loops (iterate over a sequence) and while loops (repeat while a condition is True).

Functions

• Functions — A function is a named, reusable block of code defined with the def keyword.
• Lambda Functions — One-Line Functions — A lambda function is a small, anonymous (nameless) function defined in a single line.

Object-Oriented Programming

• Classes & Objects — Object-Oriented Programming (OOP) models real-world entities as objects.

Error Handling

• Exception Handling — An exception is an error that occurs during program execution.

Core Python

• Numbers & Type Casting — Python has three numeric types: int (whole numbers like 5, -3, 1000), float (decimals like 3.14, -0.5, 2.0), and complex (for advanced maths like 3+4j — rarely needed at school level).
• String Methods — Powerful Text Operations — Python strings are objects with built-in methods (functions) you can call using dot notation: text.method().
• Tuples and Sets — Python has 4 main collection types: list (ordered, mutable), tuple (ordered, immutable), set (unordered, no duplicates), dict (key-value pairs).
• List Comprehensions — Pythonic Data Processing — List comprehension is a concise way to create lists in Python.
• Modules & Packages — Reusing Code — A module is a Python file (.py) containing functions, classes, and variables that you can reuse across multiple programs.

Professional Python

• File Handling — Read, Write, CSV & JSON — File handling lets Python programs read data from files and write results back — crucial for any real application.
• Type Hints & Annotations — Type hints tell Python (and your editor) what type of data a variable or function expects and returns.
• Multithreading & Multiprocessing — Python's threading module allows concurrent execution for I/O-bound tasks (network, file, database operations).
• Advanced Error Handling — Beyond basic try/except, Python allows chaining exceptions with raise X from Y to show the original cause.
• Collections Module — Counter, defaultdict, deque — The collections module has specialised containers more efficient than plain dict/list for common patterns.
• File Handling — Read, Write, CSV & JSON — File handling lets Python programs read data from files and write results back — crucial for any real application.
• Unit Testing — Automated Tests with unittest — Unit testing verifies individual functions work correctly.

Algorithms

• Sorting & Searching Algorithms — Sorting and searching are the most fundamental algorithms in computer science.
• Recursion — Functions That Call Themselves — Recursion is a programming technique where a function solves a problem by calling itself with a smaller version of the same problem.

Mini Projects

• Mini Project — Student Marks Calculator — A marks calculator is a practical real-world project that uses variables, lists, dictionaries, functions, and string formatting all together.
• Mini Project: Text Analyzer — This mini project builds a complete text analysis tool that processes any piece of text and produces meaningful statistics — combining strings, collections, regex, and sorting in one practical program.
• Mini Project: CLI Todo App — This project builds a full command-line Todo application that persists tasks to a JSON file — pulling together functions, JSON, pathlib, dataclasses, and user interaction patterns.

Best Practices

• Common Python Errors & How to Fix Them — Understanding error messages is a crucial skill.

Functional Python

• Generators & Iterators — A generator is a function that returns values one at a time using the yield keyword, instead of returning all values at once.
• Decorators — A decorator is a function that wraps another function to add extra behaviour without modifying the original function code.
• Context Managers (with statement) — A context manager controls the setup and teardown of resources.
• Advanced Comprehensions — Python supports list, dict, set, and generator comprehensions — all providing a concise way to create collections from existing ones.
• Iterators & the Iterator Protocol — An iterator implements __iter__() and __next__() — the iterator protocol.
• Closures — Functions that Remember — A closure is a function that captures variables from its enclosing scope.
• map, filter, reduce & zip — Functional Programming — Python supports functional programming through map(), filter(), reduce(), and zip().

Text Processing

• Regular Expressions (re module) — Regular expressions (regex) are patterns for matching, searching, and extracting text.

Object-Oriented Python

• Dunder / Magic Methods — Dunder methods (double underscore methods) let you define how your objects behave with Python operators and built-in functions.
• Dataclasses — @dataclass is a Python 3.7+ decorator that automatically generates __init__, __repr__, and __eq__ for a class based on annotated fields.
• Abstract Classes & Interfaces — An abstract class defines a blueprint that subclasses must implement.
• Dataclasses — Advanced Features
• Design Patterns in Python — Design patterns are proven, reusable solutions to commonly recurring software design problems.

Python Features

• Walrus Operator (:=) — The walrus operator (:=), introduced in Python 3.8, is the "assignment expression" operator.
• Context Managers — Custom with Statement — A context manager controls resource acquisition and release using the with statement.
• Type Hints & Annotations — Type hints (PEP 484, Python 3.5+) let you annotate variables and functions with expected types.

Python Standard Library

• itertools Module — itertools is a standard-library module of fast, memory-efficient iterator building blocks.
• collections Module (Counter, defaultdict, deque) — The collections module provides specialised container types that extend Python's built-in dict, list, and tuple for common patterns — more efficient and expressive than implementing them yourself.
• pathlib — Modern File Paths — pathlib (Python 3.4+) provides Path objects for working with file system paths — an object-oriented alternative to string-based os.path manipulation.
• Regular Expressions — Practical Patterns — Regular expressions (regex) are patterns that describe text — they let you search, validate, extract, and replace complex string patterns with a single expressive rule.
• JSON — Reading & Writing JSON Data — JSON (JavaScript Object Notation) is the universal data exchange format for web APIs, configuration files, and data storage.

Who this Python course is for

School and college students in India starting from zero, and anyone revising Python for placements, board practicals or a project. There is no prerequisite beyond being able to type — the first lessons assume no programming background at all.

How to study this course

1. Read the lesson, then run the example in the built-in editor before moving on — reading code is not the same as writing it.
2. Try the practice task at the end of each lesson. Getting it wrong and fixing it is where the learning happens.
3. When you are stuck, ask the free AI Tutor to explain that specific line rather than skipping ahead.

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