Graph theory algorithms visualized

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1. Introduction

1.1 introduction.pptx

1. Introduction to graph theory.mp4

05:33

2. [IMPORTANT] Before we start.html

3. Python crash course (optional).html

4.1 types and terms.pptx

4. Terminology and types of graphs.mp4

20:17

2. Graph representation

1.1 adjacency list.pptx

1.2 graph adj list.zip

1.3 graphadjlist.zip

1. Adjacency list representation.mp4

11:50

2.1 adjacency matrix.pptx

2.2 graph adj mat.zip

2.3 graphadjmat.zip

2. Adjacency matrix representation.mp4

08:22

3.1 adj list vs adj mat.pptx

3. Adjacency list vs adjacency matrix.mp4

12:13

4. Quiz Adjacency lists and matrices.html

3. Graph traversal

1.1 depth-first search.pptx

1.2 dfs visualization.zip

1.3 dfs.zip

1.4 dfs.zip

1. Depth-first search (DFS) algorithm.mp4

19:45

2. Problem Path exists in a graph.html

3.1 dfs path exists.pptx

3.2 dfs path exists.zip

3.3 dfspathexists.zip

3. Solution Path exists in a graph.mp4

08:56

4.1 bfs visualization.zip

4.2 bfs.zip

4.3 bfs.zip

4.4 breadth first search.pptx

4. Breadth-first search (BFS) algorithm.mp4

14:54

5. Problem Minimum edges from start to end.html

6.1 bfs path exists.pptx

6.2 bfs min edges.zip

6.3 bfsminedges.zip

6. Solution Minimum edges from start to end.mp4

07:30

7.1 bfs grid.zip

7.2 bfsgrid.zip

7.3 dfs and bfs in hidden.pptx

7.4 dfs grid.zip

7.5 dfsgrid.zip

7.6 Flood fill algorithm YouTube video.html

7. DFS and BFS in implicit graphs.mp4

10:13

4. Topological sort

1.1 topological sort intro.pptx

1. What is topological sort.mp4

09:01

2.1 topological sort dfs.pptx

2.2 topsort dfs.zip

2.3 topsortdfs.zip

2. DFS-based topological sort algorithm.mp4

16:17

3.1 topological sort bfs.pptx

3.2 topsort bfs.zip

3.3 topsortbfs.zip

3. BFS-based topological sort algorithm (Kahns algorithm).mp4

14:14

4. Problem Find all possible recipes.html

5.1 find recipes.pptx

5.2 find possible recipes.zip

5.3 findpossiblerecipes.zip

5. Solution Find all possible recipes problem.mp4

12:57

6. Quiz Topological sort.html

5. Shortest path problem

1.1 shortest path intro.pptx

1. Introduction.mp4

03:34

2.1 Binary heaps YouTube video.html

2.2 dijkstra.zip

2.3 dijkstra.zip

2.4 dijkstras algorithm.pptx

2.5 fibonacciheap.zip

2.6 tuple.zip

2. Dijkstras algorithm.mp4

44:27

3.1 bellman ford.pptx

3.2 bellman ford.zip

3.3 bellmanford.zip

3.4 edge.zip

3.5 tuple.zip

3. Bellman-Ford algorithm.mp4

32:30

4.1 floyd warshall.pptx

4.2 floyd warshall.zip

4.3 floydwarshall.zip

4. Floyd-Warshall algorithm.mp4

33:40

5.1 johnson.zip

5.2 johnson.zip

5.3 johnsons.pptx

5. Johnsons algorithm.mp4

13:01

6.1 shortest path with unweighted graphs.pptx

6.2 shortest path unweighted.zip

6.3 shortestpathunweighted.zip

6. Shortest path in unweighted graphs.mp4

07:19

7.1 dag shortest path.pptx

7.2 shortest path dag.zip

7.3 shortestpathdag.zip

7. Shortest path in directed acyclic graphs.mp4

15:09

6. Trees

1.1 is tree.zip

1.2 istree.zip

1.3 trees introduction.pptx

1. What is a tree.mp4

12:27

2.1 graph to out-tree.pptx

2.2 to out tree.zip

2.3 toouttree.zip

2. Out-trees (arborescence) and graph to out-tree conversion.mp4

06:32

3.1 distance k.zip

3.2 distancek.zip

3.3 nodes at a distance k.pptx

3. Solution All nodes distance k in a tree problem.mp4

08:33

4. Quiz What is a tree.html

7. Minimum spanning trees

1.1 find spanning tree.zip

1.2 findspanningtree.zip

1.3 mst-intro.pptx

1. What is a (minimum) spanning tree.mp4

08:57

2.1 fibonacciheap.zip

2.2 prim.zip

2.3 prim.pptx

2.4 prim.zip

2.5 tuple.zip

2. Prims algorithm.mp4

25:26

3.1 Disjoint-set data structure YouTube video.html

3.2 disjointset.zip

3.3 kruskal.zip

3.4 kruskal.pptx

3.5 kruskal.zip

3. Kruskals algorithm.mp4

13:08

4. Problem Min cost to connect all points.html

5.1 min cost to connect all points.pptx

5.2 min cost connect.zip

5.3 mincostconnect.zip

5. Solution Min cost to connect all points problem.mp4

05:31

6. Quiz Minimum spanning trees.html

8. Eulerian and Hamiltonian pathscycles

1.1 eulerian trail intro.pptx

1. What is a Eulerian pathcycle.mp4

09:15

2.1 hierholzer.zip

2.2 hierholzer.pptx

2.3 hierholzer.zip

2. Hierholzers algorithm.mp4

19:47

3. Problem Reconstruct itinerary.html

4.1 reconstruct itinerary.pptx

4.2 reconstruct itinerary.zip

4.3 reconstructitinerary.zip

4. Solution Reconstruct itinerary.mp4

05:35

5.1 hamiltonian paths.pptx

5. What is a Hamiltonian pathcycle.mp4

06:29

6.1 hamiltonian backtracking.pptx

6.2 hamiltonian backtracking.zip

6.3 hamiltonianbacktracking.zip

6. Hamiltonian pathcycle finding algorithms.mp4

10:12

9. Graph coloring

1.1 graph coloring.pptx

1. Introduction to graph coloring.mp4

14:54

2.1 is bipartite.pptx

2.2 two colorability.zip

2.3 twocolorability.zip

2. Checking 2-colorability (bipartite graph).mp4

09:34

3.1 k colorability.pptx

3.2 k colorability.zip

3.3 kcolorability.zip

3. Checking k-colorability with backtracking.mp4

12:12

4.1 greedy coloring.pptx

4.2 greedy coloring.zip

4.3 greedycoloring.zip

4. Greedy coloring.mp4

11:27

5.1 coloring heuristics.pptx

5.2 dsatur.zip

5.3 dsatur.zip

5.4 welsh powell.zip

5.5 welshpowell.zip

5. Heuristics (Welsh-Powell, DSatur).mp4

20:53

6. Problem Sudoku solver.html

7.1 sudoku coloring.pptx

7.2 sudoku solver.zip

7.3 sudokusolver.zip

7. Solution Lets make a Sudoku solver.mp4

12:14

8. Quiz Graph coloring.html

Description

Unleash the power of graph theory with cutting-edge algorithms

What You'll Learn?

Learn graphs terminology and representation
Learn graph traversal
Learn algorithms related to various graph theory topics (shortest paths, minimum spanning trees...)
Solve graph related coding interview problems

Who is this for?

Computer science students

Data science beginners

Software development beginners

More details

Description
This Graph theory algorithms will teach students the fundamental concepts and algorithms of graph theory with real life examples and eye-appealing visualizations. The course will cover topics such as graph representation, graph traversal, topological sort, shortest paths, minimum spanning trees, graph coloring... With a total of more than 20 covered algorithms.
Discussed algorithms will be implemented in detail by using a programming language to give a better understanding for students. Captions, practice problems, quizzes, slides, and source code will also be here to make the learning experience way better.Â
By the end of the course, students will have a strong understanding of graph algorithms and be able to apply their knowledge to solve problems in computer science, mathematics, and beyond.
This course is ideal for students who are looking to pursue careers in computer science, mathematics, or related fields, as well as for professionals who want to expand their knowledge of graph theory algorithms.

Covered algorithms:
Graph traversal:
Depth-first search
Breadth-first search
Topological sorting:
Depth-first search based topological sort
Breadth-first search based topological sort (Kahn's algorithm)
Shortest path:
Dijkstra's algorithm
Bellman-Ford algorithm
Floyd-Warshall algorithm
Johnson's algorithm
Shortest path for unweighted graphs algorithm
Shortest path for directed acyclic graphs (1st approach) algorithm
Shortest path for directed acyclic graphs (2nd approach) algorithm
Trees and minimum spanning trees:
Spanning tree algorithm
Graph to out-tree algorithm
Prim's algorithm
Kruskal's algorithm
Eulerian/Hamiltonian paths and cycles:
Hierholzer's algorithm
Hamiltonian cycle backtracking algorithm
Graph coloring:
2-colorability algorithm
k-colorability backtracking algorithm
Greedy coloring algorithm
Welsh-Powell heuristic
DSatur heuristic
Who this course is for:
Computer science students
Data science beginners
Software development beginners

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