Algorithms

The Algorithms category focuses on understanding common algorithms by implementing and using them in Python. Instead of heavy theory, this category emphasizes intuition, problem-solving, and real-world use cases. Algorithms are a key part of writing efficient code, processing data, and understanding how machine learning methods work under the hood.

What You’ll Learn

In this category, you’ll learn how algorithms solve problems and how to apply them effectively in Python.

By the end of Algorithms, you’ll be able to:

• Understand how common algorithms work conceptually
• Implement algorithms using clean Python code
• Analyze time and space complexity at a practical level
• Choose appropriate algorithms for different problems
• Recognize algorithmic patterns used in data processing and ML

Learning Path

Algorithm Foundations

Learn the core ideas behind algorithms and how to reason about efficiency. Lessons include:

• What Is an Algorithm?
• Time and Space Complexity (Big-O Basics)
• Algorithmic Thinking and Problem Decomposition
• Tradeoffs Between Readability and Performance
• When Optimization Matters

Searching and Sorting Algorithms

Learn classic algorithms that appear frequently in real-world code and interviews. Lessons include:

• Linear and Binary Search
• Bubble, Selection, and Insertion Sort
• Merge Sort and Quick Sort
• Built-in Python Sorting Under the Hood
• Choosing the Right Sorting Strategy

Data Structure–Based Algorithms

Learn algorithms that rely on core data structures. Lessons include:

• Algorithms with Lists and Arrays
• Stack and Queue Algorithms
• Hash-Based Algorithms with Dictionaries
• Set Operations and Use Cases
• Practical Uses of Heaps

Recursive and Divide-and-Conquer Algorithms

Learn how problems can be broken down into smaller subproblems. Lessons include:

• Understanding Recursion
• Common Recursive Patterns
• Divide-and-Conquer Strategy
• Recursion vs Iteration
• Avoiding Common Recursion Pitfalls

Algorithms in Data and Machine Learning

Learn how algorithms are applied in data processing and ML workflows. Lessons include:

• Feature Selection Algorithms
• Clustering Algorithm Intuition
• Optimization Algorithms Overview
• Distance Metrics and Similarity Measures
• Algorithm Efficiency in ML Pipelines

Common Coding Interview Questions

• Two Sum: Find two numbers in a sorted array that add up to a target.
• Longest Substring Without Repeating Characters: Find the length of the longest substring

Who This Category Is For

This category is ideal if you:

• Know basic Python and want to improve problem-solving skills
• Want to write more efficient and scalable code
• Are preparing for technical interviews
• Want deeper insight into how ML and data tools work internally

Begin with algorithm foundations and work through the lessons in order. Each lesson focuses on practical understanding using Python examples you can run and modify.