Advanced Algorithms, Dynamic Programming & Graph Algorithms
This course features Coursera Coach!
A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
Mastering advanced algorithms is essential for solving complex problems in real-world applications. In this course, you’ll dive deep into critical concepts such as dynamic programming, graph theory, heap operations, and bit manipulation techniques. Each section builds on your knowledge, ensuring a comprehensive understanding that will be indispensable in interviews, competitive programming, and everyday coding tasks.
The course begins by introducing heaps, providing hands-on lessons on implementing heaps, inserting and deleting elements, and solving problems like finding the kth largest element in an array. From there, you'll move to dynamic programming, tackling classical problems such as "Climbing Stairs," "Coin Change," and "Longest Common Subsequence," learning the techniques to optimize recursive algorithms with memorization and tabulation. You’ll also explore graph algorithms including BFS, DFS, Dijkstra's, and Bellman-Ford for shortest path solutions, as well as Minimum Spanning Trees with Prim’s Algorithm.
Designed for anyone looking to deepen their algorithmic knowledge, this course is suitable for intermediate learners with a basic understanding of data structures. No prior experience with dynamic programming or advanced graph algorithms is required, but a solid grasp of programming basics will be beneficial.
By the end of the course, you will be able to efficiently solve complex problems using dynamic programming, implement advanced graph algorithms, and apply heaps to optimize your solutions. You’ll also gain hands-on experience with Leetcode-style problems that are frequently encountered in technical interviews.
Watch on External: Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Algorithm Basics
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
I Built 11 Models to Predict the 2026 World Cup. They Crown Four Different Champions.
Towards Data Science
The System Always Knows: Why Local Efficiency and System Performance Are Not the Same Problem
Towards Data Science
MLOps Systems: Feature Engineering: Moving Averages for Noise-Resilient Trend Signals in Python
Medium · Machine Learning
MLOps Systems: Feature Engineering: Moving Averages for Noise-Resilient Trend Signals in Python
Medium · Python
🎓
Tutor Explanation
DeepCamp AI