Advanced Algorithms and Data Structures in Python | Udemy

What you'll learn
Have a good grasp of algorithmic thinking
Be able to develop your own algorithms
Be able to detect and correct inefficient code snippets
Understand Fenwick trees
Understand caches (LRU caches and Splay Trees)
Understand tries and ternary search trees
Understand substring search algorithms (Rabin-Karp method, KMP algorithm and Z algorithm)
Understand the Hamiltonian cycle problem (and travelling salesman problem)
Understand Eulerian cycle problem

This course is for those who are interested in computer science and want to implement the algorithms and given data structures in Python. In every chapter you will learn about the theory of a given data structure or algorithm and then you will implement them from scratch.

Chapter 1: Binary Indexed Trees (Fenwick Trees)

theory behind the binary indexed tree or Fenwick tree data structure

how to use this data structure in computer vision and artificial intelligence

implementation in Python

Chapter 2: LRU Caches

what are caches and why are they so important

how to use doubly linked lists to implement caches

theory behind LRU caches

implementation in Python

Chapter 3: Splay Trees

what are splay trees

how to achieve caches with splay trees

Chapter 4: B-Trees

external memory and internal memory (RAM)

data structures for the external memory

trees with multiple children and multiple keys

what are B-tree data structures?

Chapter 5: Prefix Trees (Tries)

what are tries or prefix trees

real world applications of tries

autocomplete feature of tries

sorting with tries

IP routing

Chapter 6: Ternary Search Trees

what are ternary search trees

boggle game with tries

Chapter 7: Substring Search Algorithms

what are substring search algorithms and why are they important in real world softwares

brute-force substring search algorithm

hashing and Rabin-Karp method

Knuth-Morris-Pratt substring search algorithm

Z substring search algorithm (Z algorithm)

implementations in Python

Chapter 8: Topological Ordering

what is topological ordering (topological sort)?

topological ordering implementation with depth-first search

Chapter 9: Cycle Detection

how to detect cycles in graphs?

Chapter 10: Strongly Connected Components (Tarjan's Algorithm)

what are strongly connected components?

Tarjan's algorithm with depth-first search

Chapter 11: Hamiltonian cycles (Travelling Salesman Problem)

Hamiltonian cycles in graphs

what is the travelling salesman problem?

how to use backtracking to solve the problem

meta-heuristic approaches to boost algorithms

Chapter 12: Eulerian Cycles (Chinese Postman Problem)

Eulerian cycles in graphs

what is the chinese postman problem?

Thanks for joining my course, let's get started!

Who this course is for:
This course is suited for anyone who has some basic knowledge in Python and interested in algorithms and data structures




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