321 Blog Posts To Learn About Algorithms

2026-07-06 · Source: HackerNoon · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, extended

Summary

HackerNoon presents a comprehensive collection of 321 free blog posts dedicated to learning algorithms, ordered by reader engagement data. This extensive resource covers fundamental concepts such as Big O notation, various sorting algorithms like Bubble Sort, Merge Sort, and Quick Sort, and essential data structures including Linked Lists, Binary Trees, Heaps, and Skip Lists. The posts also delve into advanced topics like dynamic programming, graph traversal algorithms (BFS, DFS, Dijkstra, A*), machine learning algorithms (Gradient Descent, SVM, Q-learning), and cryptographic techniques (RSA, SHA-256). Practical applications are highlighted through numerous LeetCode problem-solving guides, image processing algorithms, and discussions on algorithmic trading and the ethical implications of AI.

Key takeaway

For software engineers or students preparing for technical interviews or seeking to deepen your algorithmic understanding, this curated list of 321 HackerNoon blog posts provides a structured, engagement-ranked resource. Utilize these articles to grasp core concepts, practice problem-solving on platforms like LeetCode, and explore advanced topics from machine learning to cryptography, ensuring a well-rounded and practical skill set for career growth.

Key insights

HackerNoon offers 321 reader-ranked blog posts covering a broad spectrum of algorithms and data structures for diverse learning needs.

Principles

• Understanding algorithms and data structures is crucial for effective software development.
• Algorithmic complexity (e.g., Big O) is fundamental for performance analysis.
• Practical application through coding challenges (e.g., LeetCode) enhances skill development.

Method

Access 321 HackerNoon blog posts, ordered by reader engagement, to learn algorithms and data structures, from foundational concepts to advanced applications.

In practice

• Solve LeetCode problems using various algorithms (e.g., Merge k Sorted Lists, Number of Islands).
• Implement sorting algorithms like Bubble Sort, Merge Sort, and Quick Sort.
• Apply dynamic programming, graph traversal, and bitwise operations to coding challenges.

Topics

• Algorithms
• Data Structures
• LeetCode Problems
• Algorithmic Complexity
• Dynamic Programming
• Coding Interviews

Best for: Software Engineer, AI Student, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.