Recursion, Backtracking and Dynamic Programming in Python Download
Learn Competitive Programming, Recursion, Backtracking, Divide and Conquer Methods and Dynamic Programming in Python
What you’ll learn
 Understanding recursion
 Understand backtracking
 Understand dynamic programming
 Understand divide and conquer methods
 Implement 15+ algorithmic problems from scratch
 Improve your problem solving skills and become a stronger developer
Requirements
 Basic Python
Description
This course is about the fundamental concepts of algorithmic problems focusing on recursion, backtracking, dynamic programming and divide and conquer approaches. 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 – RECURSION

what are recursion and recursive methods

stack memory and heap memory overview

what is stack overflow?

Fibonacci numbers

factorial function

tower of Hanoi problem
Section 2 – SEARCH ALGORITHMS

linear search approach

binary search algorithm
Section 3 – SELECTION ALGORITHMS

what are selection algorithms?

Hoare’s algorithm

how to find the kth order statistics in O(N) linear running time?

quickselect algorithm

median of medians algorithm

the secretary problem
Section 4 – BIT MANIPULATION PROBLEMS

binary numbers

logical operators and shift operators

checking even and odd numbers

bit length problem

Russian peasant multiplication
Section 5 – BACKTRACKING

what is backtracking?

nqueens problem

Hamiltonian cycle problem

coloring problem

knight’s tour problem

maze problem

Sudoku problem
Section 6 – DYNAMIC PROGRAMMING

what is dynamic programming?

knapsack problem

rod cutting problem

subset sum problem

Kadane’s algorithm

longest common subsequence (LCS) problem
Section 7 – OPTIMAL PACKING

what is optimal packing?

bin packing problem
Section 8 – DIVIDE AND CONQUER APPROACHES

what is the divide and conquer approach?

dynamic programming and divide and conquer method

how to achieve sorting in O(NlogN) with merge sort?

the closest pair of points problem
Section 9 – Substring Search Algorithms

substring search algorithms

bruteforce substring search

Z substring search algorithm

RabinKarp algorithm and hashing

KnuthMorrisPratt (KMP) substring search algorithm
Section 10 – COMMON INTERVIEW QUESTIONS

top interview questions (Google, Facebook and Amazon)

anagram problem

palindrome problem

integer reversion problem

dutch national flag problem

trapping rain water problem
Section 11 – Algorithms Analysis

how to measure the running time of algorithms

running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations

complexity classes

polynomial (P) and nondeterministic polynomial (NP) algorithms
In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together from scratch in Python.
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
 Anyone preparing for programming interviews or interested in improving their problem solving skills