Python Tutorial

Python inheritance allows you to build new classes by reusing and extending the functionality of existing ones. Learn how to design parent-child class relationships, implement inheritance patterns, and apply techniques such as method overriding.

Master feature extraction techniques with hands-on Python examples for image, audio, and time series data. Learn how to transform raw data into meaningful features and overcome common challenges in machine learning applications.

Learn to create and customize Seaborn barplots in Python. Master essential techniques for visualizing categorical data relationships, from basic plots to advanced features.

Examine the mathematical principles behind random walks and explore their forms, from one-dimensional paths to biased and Gaussian models. Use Python to discover how these stochastic processes inform real-world phenomena in biology, physics, and finance.

Learn the hill climbing algorithm in Python. This guide covers types, limitations, and real-world AI applications with code examples.

Learn to code an unbeatable Tic-tac-toe AI using the Minimax algorithm in Python. This tutorial covers theory, implementation, and optimization, ideal for game AI enthusiasts.

Learn about Python lambda functions, their purpose, and when to use them. Includes practical examples and best practices for effective implementation.

A beginner-friendly guide to TorchRL for deep reinforcement learning—learn to build RL agents with PyTorch through practical examples.

Learn about Python abstract classes, their purpose, and how to use the `abc` module to enforce consistent interfaces. Includes practical examples and best practices for effective implementation.

Discover how the Fibonacci sequence works. Explore its mathematical properties and real-world applications.

Discover how slicing can effortlessly extract, rearrange, and analyze your data. Harness negative indices, multi-dimensional slices, and advanced step values for full precision.

Learn to use DeepEval to create Pytest-like relevance tests, evaluate LLM outputs with the G-eval metric, and benchmark Qwen 2.5 using MMLU.