In-depth Python tutorials covering programming fundamentals, data science workflows, and machine learning concepts with real examples.
Learn the fundamentals of Python programming, including syntax, data types, control flow, functions, and writing clean, readable code. This category builds the foundation needed for data processing and machine learning.
Learn core algorithms through practical Python examples, focusing on problem-solving, efficiency, and real-world applications.
Learn how to work with data using Python, including reading files, handling sensor data, preprocessing, normalization, and basic feature extraction. Focused on preparing real-world data for machine learning.
Learn core machine learning concepts and algorithms using Python. Covers model training, evaluation, and lightweight models suitable for deployment on resource-constrained devices.
Learn the fundamentals of TinyML and how machine learning runs on microcontrollers. Covers constraints, workflows, model optimization, and the transition from Python-trained models to embedded systems.
Hands-on, end-to-end projects that combine Python, data processing, machine learning, and TinyML. Learn by building real examples from data collection to on-device inference.
Discover five industry-relevant Python projects that reflect the realities of data science and machine learning in 2026. From graph neural networks and multimodal sentiment analysis to predictive maintenance, ethical NLP, and deepfake detection, this guide helps intermediate Python developers build a portfolio that stands out to recruiters.
Two Sum is more than a beginner coding problem—it teaches core algorithmic thinking, hash map usage, and time–space trade-offs. This guide walks through brute-force and optimized solutions in Python, explaining complements, hash maps, and complexity analysis in a clear, interview-focused way.
Python functions are the foundation of clean, maintainable code. This guide helps you move away from spaghetti code by applying the DRY principle, understanding function anatomy, mastering arguments and scope, and using professional practices like docstrings and type hints to write reusable Python code.
Python tuples are more than just lists with parentheses. Built around immutability and data integrity, tuples enable safer data sharing, better performance, and powerful features like unpacking, hashability, and named tuples. This guide explains when and why to use tuples, how they differ from lists, and how they can make your Python code clearer and more reliable.
An in-depth and easy-to-follow guide to solving the Longest Substring Without Repeating Characters problem using the sliding window pattern in Python. This article explains the intuition behind pointers and hash maps, walks through a real example step by step, and shows how to optimize a naive quadratic solution into an efficient linear-time algorithm commonly expected in technical interviews.
Python dictionaries are a core data structure that power fast lookups and clean data modeling in real-world applications. This guide takes you beyond the basics, covering best practices, built-in methods, performance insights, and advanced techniques like dictionary comprehensions and defaultdict to help you write more efficient and professional Python code.
Python lists are one of the most powerful and commonly used data structures in the language. This guide takes beginners beyond the basics, explaining how lists work in memory, how to modify and slice them, and how to avoid common pitfalls while writing clean, readable Python code.