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
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 - SEARCHALGORITHMS
linear search approach
binary search algorithm
Section 3 - SELECTIONALGORITHMS
what are selection algorithms?
Hoare's algorithm
how to find the k-th order statistics in O(N) linear running time?
quickselect algorithm
median of medians algorithm
the secretary problem
Section 4 - BITMANIPULATION 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?
n-queens problem
Hamiltonian cycle problem
coloring problem
knight's tour problem
maze problem
Sudoku problem
Section 6 - DYNAMICPROGRAMMING
what is dynamic programming?
knapsack problem
rod cutting problem
subset sum problem
Kadane's algorithm
longest common subsequence (LCS) problem
Section 7 - OPTIMALPACKING
what is optimal packing?
bin packing problem
Section 8 - DIVIDEANDCONQUERAPPROACHES
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
brute-force substring search
Z substring search algorithm
Rabin-Karp algorithm and hashing
Knuth-Morris-Pratt (KMP) substring search algorithm
Section 10 - COMMONINTERVIEWQUESTIONS
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 non-deterministic 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!
