This course is about getting hands-on experience solving algorithms in Python.

As a developer, mastering the concepts of algorithms and being proficient in implementing them is essential to improving problem-solving skills. This course aims to equip you with an in-depth understanding of algorithms and how they can be utilized for problem-solving in Python.

Starting with the basics, you'll gain a foundational understanding of what algorithms are, with topics ranging from simple multiplication algorithms to analyzing algorithms. Then, you’ll delve into more advanced topics like recursion, backtracking, dynamic programming, and greedy algorithms. You'll also learn about basic graph algorithms, depth-first search, shortest paths, minimum spanning trees, and all-pairs shortest paths. 

By the end of this course, you'll have acquired a wide range of skills that will significantly enhance your ability to solve problems efficiently in Python. Through this course, not only will you improve your coding skills, but you will also gain confidence in tackling complex problems.

Mastering Algorithms for Problem Solving in Python

How many steps are required to solve the Tower of Hanoi problem having $n$ disks?

How many steps are required to solve the Tower of Hanoi problem having <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math>n disks?

What is the time complexity of the merge-sort algorithm? 

What is the time complexity of the merge-sort algorithm?

In what type of sorting algorithm do we need to choose a pivot element?

In what type of sorting algorithm do we need to choose a pivot element?

What is the time complexity of the quick-sort algorithm in the worst case?

What is the time complexity of the quick-sort algorithm in the worst case?

Merge-sort and quick-sort follow which same three-step approach?

Merge-sort and quick-sort follow which same three-step approach?

Reinforce your understanding of recursive functions and their applications in problem solving.

Getting Started

Introduction to Algorithm

Recursion

Backtracking

Dynamic Programming

Greedy Algorithms

Prove Your Skills: A Five-Chapter Assessment

Basic Graph Algorithms

Depth-First Search

Minimum Spanning Trees

Shortest Paths

All-Pairs Shortest Paths

Pushing Your Limits: A Comprehensive Assessment

Wrapping up

Quiz: Recursion