The Design and Analysis of Algorithm Masterclass 2023

Algorithm & Data Structures will help you to Crack Coding Interviews (C/C++Java/Python) Learn Algorithm Data Structures
The Design and Analysis of Algorithm Masterclass 2023
File Size :
12.61 GB
Total length :
12h 33m



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The Design and Analysis of Algorithm Masterclass 2023

What you’ll learn

Enhance Your Logical Thinking Abilities
Learn various Popular Data Structures and their Algorithms.
Knowing Algorithm Well helps you to Solve the Problem in a Better Way.
Learn Big O, Big Omega Big Theta Notation
Linear Search, Recurrence Relations
Factorial, Tail Recursion
Towers of Hanoi
Merge Sort, Quick Sort, Heap Sort
Knapsack Problem, Minimum Spanning Tree: Kruskal’s Algorithm, Minimum Spanning Tree: Prim’s Algorithm
Huffman’s Codes – Building Huffman Tree, Dijkstra’s Algorithm,Bellman Ford Algorithm, Floyd Warshall Algorithm
Brute Force Matcher
Pattern Pre-Processing
The Knuth Morris Pratt Algorithm
n-Queens Algorithm
Graph Coloring, Hamiltonian Cycles
0/1 Knapsack Problem
15 Puzzle Problem
NP Completeness and Approximation Algorithms
Will get to know Real time uses of all Algorithm
Get the answer of your “WHY” part behind use of every Algorithm

The Design and Analysis of Algorithm Masterclass 2023


Idea of Programming (Good To Have – NOT Mandatory)
Some Basic Mathematics (Good To Have – NOT Mandatory)
Basic Concepts of Data Structures (Good To Have – NOT Mandatory)
Little Familiarity with Graph Theory (Good To Have – NOT Mandatory)


The algorithm is used everywhere. People Don’t know how Complex algorithms they are executing when doing there day to day tasks like Riding a Bi-Cycle, Travelling from one place to another even Watering Gardens. If you are Coder then the Knowledge of algorithms is Very much important for you. The knowledge of Algorithm teach you How to Think to solve a Problem?The algorithm is the concept that differentiates one average software engineer and one better software engineer. In our daily life in the industry, we used different kinds of algorithms to make the system faster, better, and more efficient.But the problem is 90% of the freshers and graduates don’t have the basic knowledge of algorithms. That is the reason we make this Design and Analysis of algorithm Masterclass.What you are Going to Learn?Asymptotic Notations, Recursion, Divide and Conquer, Dynamic Programming, Dijkstra’s, Bellman-Ford, Floyd Warshall Algorithm, Kruskal’s Algorithm, Knapsack Problem, String Matching with Finite Automaton, Heap sort, Huffman Codes, n-Queens Algorithm, Rat in Maze, 0/1 Knapsack Problem, 15 Puzzle Problem, NP-Completeness, Approximation Algorithms12 hours of HD content [Updated on 2022 December]Assignment [Updated]Study Note [Updated]Certificate Every Single Day we will check your questions and solve your queries.Topics covered :Enhance Your Logical Thinking AbilitiesLearn various Popular Data Structures and their Algorithms.Knowing Algorithm Well helps you to Solve the Problem in a Better Way.Learn Big O, Big Omega Big Theta NotationLinear Search, Recurrence RelationsFactorial, Tail RecursionTowers of HanoiMerge Sort, Quick Sort, Heap SortKnapsack Problem, Minimum Spanning Tree: Kruskal’s Algorithm, Minimum Spanning Tree: Prim’s AlgorithmHuffman’s Codes – Building Huffman Tree, Dijkstra’s Algorithm, Bellman-Ford Algorithm, Floyd Warshall AlgorithmBrute Force MatcherPattern Pre-ProcessingThe Knuth Morris Pratt Algorithmn-Queens AlgorithmGraph Coloring, Hamiltonian Cycles0/1 Knapsack Problem15 Puzzle ProblemNP-Completeness and Approximation AlgorithmsWill get to know Real-time uses of all AlgorithmGet the answer of your “WHY” part behind the use of every Algorithm


Section 1: Introduction & Overview

Lecture 1 About The Instructor

Lecture 2 Objective of the Course

Lecture 3 Prerequisite to take the Course

Lecture 4 Course Curriculum Overview

Section 2: Introduction to Algorithms

Lecture 5 1.Introduction to Algorithms

Section 3: Asymptotic Notations

Lecture 6 2.Big O, Big Omega and Big Theta

Lecture 7 Problems on Big O

Lecture 8 Algorithmic Complexity with Asymptotic Notations

Section 4: Recursion

Lecture 9 Linear Search, Greatest Common Divisor

Lecture 10 Factorial, Tail Recursion

Lecture 11 Recurrence Relations, Substitution Method

Lecture 12 Towers of Hanoi

Section 5: Divide and Conquer

Lecture 13 Binary Search

Lecture 14 Master Method

Lecture 15 Tiling a Defective Chessboard

Lecture 16 Merge Sort Part #1

Lecture 17 Merge Sort Part #2

Lecture 18 Quick Sort

Section 6: Dynamic Programming

Lecture 19 Fibonacci Numbers

Lecture 20 Rod Cutting

Lecture 21 Matrix Chain Multiplication

Lecture 22 Longest Common Sub Sequence

Section 7: Greedy Algorithms

Lecture 23 Knapsack Problem

Lecture 24 Minimum Spanning Tree: Kruskal’s Algorithm

Lecture 25 Disjoint Sets and Kruskal’s Algorithm

Lecture 26 Job Sequencing with Deadlines

Lecture 27 Heap

Lecture 28 Heap Sort

Lecture 29 Priority Queue

Lecture 30 Minimum Spanning Tree: Prim’s Algorithm

Lecture 31 Huffman’s Codes – Building Huffman Tree

Lecture 32 Huffman’s Codes – Printing Huffman Codes

Section 8: Shortest Path Algorithms

Lecture 33 Dijkstra’s Algorithm Part #1

Lecture 34 Dijkstra’s Algorithm Part #2

Lecture 35 Dijkstra’s Algorithm Part #3

Lecture 36 Bellman Ford Algorithm

Lecture 37 Topological Sort

Lecture 38 Shortest Path by Topological Sort

Lecture 39 Floyd Warshall Algorithm

Section 9: The Problem of String Matching

Lecture 40 Brute Force Matcher

Lecture 41 String Matching with Finite Automaton

Lecture 42 Pattern Pre-Processing

Lecture 43 The Knuth Morris Pratt Algorithm

Section 10: Backtracking

Lecture 44 Rat in Maze

Lecture 45 n-Queens Algorithm Part #1

Lecture 46 n-Queens Algorithm Part #2

Lecture 47 Graph Coloring Part #1

Lecture 48 Graph Coloring Part #2

Lecture 49 Hamiltonian Cycles I

Lecture 50 Hamiltonian Cycles II

Lecture 51 Subset Sum

Section 11: Branch & Bound

Lecture 52 Introduction to Branch and Bound

Lecture 53 0/1 Knapsack Problem

Lecture 54 The 15 Puzzle Problem

Lecture 55 Solvability of 15 Puzzles

Section 12: NP Completeness

Lecture 56 Introduction to NP Completeness

Lecture 57 Reductions

Lecture 58 The Circuit Satisfiability Problem

Lecture 59 More NP Complete Problems 1

Lecture 60 More NP Complete Problems 2

Section 13: Approximation Algorithms

Lecture 61 Approximation Algorithms

Lecture 62 The Vertex Cover Problem

Computer science students,Software working professionals who wants to learn better way to Solve a Problem

Course Information:

Udemy | English | 12h 33m | 12.61 GB
Created by: Up Degree

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