Algorithmic Problems in Python ( Updated 7/2020)-0daytown

Learn recursion, backtracking (n-queens problem etc.) and dynamic programming (knapsack problem etc.)

What you’ll learn:
Understand backtracking
Understand dynamic programming
Solve problems from scratch
Implement feedforward neural networks from scratch

Requirements
Basic Python

Description
This course is about the fundamental concepts of algorithmic problems, focusing on recursion, backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D.

Section 1:

what is recursion

stack memory and recursion

factorial numbers problem

Fibonacci numbers

towers of Hanoi problem

recursion vs iteration

Section 2:

what is backtracking

n-queens problem

Hamiltonian cycle problem

knight’s tour problem

coloring problem

NP-complete problems

Section 3:

what is dynamic programming

Fibonacci numbers

knapsack problem

coin change problem

rod cutting problem

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems one by one.

The first chapter is about recursion. Why is it crucial to know about recursion as a computer scientist? Why stack memory is crucial in recursion? We will consider several recursion related problems such as factorial problem or Fibonacci numbers. The second chapter is about backtracking: we will talk about problems such as n-queens problem or hamiltonian cycles and coloring problem. In the last chapter we will talk about dynamic programming, theory first then the concrete examples one by one: Fibonacci sequence problem and knapsack problem.

Thanks for joining the course, let’s get started!

Who this course is for
This course is meant for newbies who are not familiar with algorithmic problems in the main or students looking for some refresher

Algorithmic Problems in Python ( Updated 7/2020)