Grokking Algorithms

📘 Grokking Algorithms – (2025–2026 Edition)

📚 Grokking Algorithms (2025–2026 Edition) is a structured, syllabus-based academic resource designed for BS/CS, BS/IT, and Software Engineering students, as well as self-learners aiming to master algorithms. This edition provides detailed notes, MCQs, and quizzes to make algorithm learning simple, visual, and exam-ready. With an organized syllabus layout, students can strengthen their problem-solving skills and apply algorithmic concepts in projects, interviews, and real-world scenarios.

This edition covers both fundamental and advanced algorithmic concepts such as recursion, sorting, searching, graph traversal, greedy approaches, dynamic programming, and machine learning basics. Each chapter is carefully designed to blend theory with hands-on understanding, making it an essential study companion.

📂 Chapters & Topics

🔹 Chapter 1: Introduction to Algorithms

- What Algorithms Are

- Why Algorithms Matter

- Measuring Algorithm Efficiency

🔹 Chapter 2: Selection Sort

- How Selection Sort Works

- Step-by-Step Walkthrough

- Big O Notation

- When to Use Selection Sort

🔹 Chapter 3: Recursion

- Understanding Recursion

- Base Case and Recursive Case

- The Call Stack

- Recursive vs. Iterative Thinking

🔹 Chapter 4: Quick Sort

- Divide-and-Conquer Strategy

- How Quick Sort Works

- Choosing a Pivot

- Performance Analysis

🔹 Chapter 5: Hash Tables

- Key-Value Pairs

- Avoiding Collisions

- Hash Functions

- Practical Uses of Hash Tables

🔹 Chapter 6: Breadth-First Search

- Graph Traversal

- Finding the Shortest Path

- Queues and Graphs

- Implementation in Code

🔹 Chapter 7: Dijkstra’s Algorithm

- Weighted Graphs

- Shortest Path in Weighted Graphs

- Priority Queues

- Dijkstra’s Step-by-Step Execution

🔹 Chapter 8: Greedy Algorithms

- Making Optimal Local Choices

- Activity Selection

- Set Cover Problem

- Limitations of Greedy Approaches

🔹 Chapter 9: Dynamic Programming

- Breaking Problems into Subproblems

- Overlapping Subproblems

- Memoization

- Examples: Knapsack Problem, Longest Common Subsequence

🔹 Chapter 10: K-Nearest Neighbors

- Classification Algorithms

- Measuring Distance

- Choosing K

- Applications in Recommendation Systems

🔹 Chapter 11: Where to Go Next

- Further Reading and Topics

- Tree and Graph Algorithms

- Advanced Sorting

- Machine Learning and Beyond

🌟 Why Choose this App?

- Covers the complete Grokking Algorithms syllabus in a structured academic format.

- Includes MCQs and quizzes for effective practice.

- Provides explanations for quick revision and clarity.

- Ideal for projects, coursework, and technical interview preparation.

- Builds strong foundations in algorithmic problem-solving and thinking.

✍ This app is inspired by:

Aditya Bhargava, Edsger W. Dijkstra, Gabriel Valiente, Sebastian Raschka, Silvano Martello, Dan Hirschberg

📥 Download Now!

Get your Grokking Algorithms (2025–2026 Edition) today and start mastering algorithms with confidence!