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

## Directed acyclic graph          
A **directed acyclic graph** or **dag** is a directed graph with no directed cycles. Any vertex in a dag that has no incoming vertices is called a **source**; any vertex with no outgoing edges is called a **sink**. An isolated vertex with no incident edges at all is both a source and a sink. Every dag has at least one source and one sink but may have more than one of each. For example, in the graph with $n$ vertices but no edges, every vertex is a source, and every vertex is a sink.

# Directed acyclic graph          
A **directed acyclic graph** or **dag** is a directed graph with no directed cycles. Any vertex in a dag that has no incoming vertices is called a **source**; any vertex with no outgoing edges is called a **sink**. An isolated vertex with no incident edges at all is both a source and a sink. Every dag has at least one source and one sink but may have more than one of each. For example, in the graph with $n$ vertices but no edges, every vertex is a source, and every vertex is a sink.

Understand the different techniques used to detect cycles in a graph.

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

Detecting Cycles

Directed acyclic graph