Algorithms in JavaScript - Design techniques

Algorithms in JavaScript : Design techniques, available at $69.99, has an average rating of 4.1, with 157 lectures, based on 60 reviews, and has 1069 subscribers.

You will learn about Algorithm Design in JavaScript This course is ideal for individuals who are Want to learn Algo Designing in JavaScript It is particularly useful for Want to learn Algo Designing in JavaScript.

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Summary

Title: Algorithms in JavaScript : Design techniques

Price: $69.99

Average Rating: 4.1

Number of Lectures: 157

Number of Published Lectures: 157

Number of Curriculum Items: 157

Number of Published Curriculum Objects: 157

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Algorithm Design in JavaScript

Who Should Attend

Want to learn Algo Designing in JavaScript

Target Audiences

Want to learn Algo Designing in JavaScript

Algorithm Design Techniques : Live problem solving in Java Script

Algorithms are everywhere! One great algorithm applied sensibly can result into a System like GOOGLE!

Larry Page, founder of google designed “Page Rank” algorithm that is behind the search in google. That is why when we search on google we generally find the most relevant result on the First Page itself.

Every Computer Programmer should learn how to design algorithms which are not only correct but also efficient in terms of

TIME and SPACE!

Completer scientists have worked from 100s of years !! – (Put images of some of the scientists…)

And derived some of the techniques that can be applied to write and design algorithms!

So Why to reinvent the wheel ??

Let’s go through some of the most famous algorithm design techniques in this course!!

Once you will come to know these design techniques It will become very easy for you to approach a problem by identifying which technique to apply to solve that correctly and efficiently.

We will start this course with some measurement techniques in algorithms that is called complexity analysis so that we can measure –

The time and space in an algorithm when we design that.

Then we will start with understanding recursion and deep dive into that.

Recursion is the base of any algorithm design … because most of the algorithms has to be solved using recursion!

Recursion is executed in computers in a very special way using stack frames… we will understand all that..

There are many types of recursion and we will have a look into that.

We will solve some classic problems like the Tower of Hanoi, Binary subtree… to understand the recursion deeply…

And WE WILL WRITE THE CODE LINE BY LINE IN JAVA !! To make it very easy to understand and code…

Then we will move into another design technique backtracking !!

Backtracking algorithms are enhanced recursion where we can revert our decision from inside a recursion…

We will understand how to Identify and approach this kind of problems..

Also, we will solve some classical problems

Rat In Maze, NQueens, KnightsTour problems… and Code them LINE by LINE …

Then, We will then move to the next section

Divide and Conquer… Greedy algorithms

And will take the same approach !! To understand identify and Solve some problems… and code some classic problems.

Then there will be a very important section! Dynamic programming

That is not only important for Algorithms design but also, Interviews

This is a very favorite paradigm for the interviewer to ask questions from – We will solve a lot of problems in section along with code… and understand how to approach this kind of problem!!

All in all!

By the end of this course –

    1. You will understand how to design algorithms

    2. A lot of coding practice and design live problems in Java

    3. Algorithm Complexity analysis

AND

If you are preparing for your coding Interview or doing competitive programming

This course will be a Big help for you!

I think this is enough to create the THRILL !! I welcome you to the course and I am sure this will be fun!!

If it does not – It comes with a 30 Days money-back guarantee so don’t think twice to give it a shot…

Welcome Again !! And See you in the course.

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Lecture 2: Course Resources

Chapter 2: Introduction To Algorithms

Lecture 1: Introduction to Algorithms

Chapter 3: Complexity Analysis

Lecture 1: Section Introduction

Lecture 2: Complexity Analysis 1

Lecture 3: Complexity Analysis 2

Lecture 4: Section Summary

Chapter 4: Recurrence Relation

Lecture 1: Section Introduction

Lecture 2: Recurrence Relation

Lecture 3: Solving Recurrence Relation

Lecture 4: Masters Theorem

Lecture 5: Section Summary

Chapter 5: Thinking Recursively

Lecture 1: Section Introduction

Lecture 2: Recursion

Lecture 3: Identification

Lecture 4: Approaching

Lecture 5: Problem 01 : FindingSubstrings – Logic

Lecture 6: Problem 01 : FindingSubstrings – Live Code JavaScript

Lecture 7: Problem 01 : FindingSubstrings – Complexity Analysis

Lecture 8: Problem 02 : Tower of Hanoi – Logic

Lecture 9: Problem 02 : Tower of Hanoi – Live Code JavaScript

Lecture 10: Problem 02 : Tower of Hanoi – Complexity Analysis

Lecture 11: Problem 03 : Array Product Sum – Logic

Lecture 12: Problem 03 : Array Product Sum – Live Code JavaScript

Lecture 13: Problem 03 : Array Product Sum – Complexity Analysis

Lecture 14: Problem 04 : Binary Subtree – Logic

Lecture 15: Problem 04 : Binary Subtree : Live code

Lecture 16: Problem 04 : Binary Subtree – Complexity Analysis

Lecture 17: Why and Why not Recursion

Lecture 18: Types Of Recursion

Lecture 19: Tail Recursion

Lecture 20: Summary

Chapter 6: Backtracking

Lecture 1: Section Introduction

Lecture 2: Introduction to Backtracking

Lecture 3: Identification

Lecture 4: Approaching The Solution

Lecture 5: Problem 01 : Rat In Maze – Logic

Lecture 6: Problem 01 : Rat In Maze – Code

Lecture 7: Problem 01 : Rat In Maze – Complexity Analysis

Lecture 8: Problem 02 : N-Queens – Logic

Lecture 9: Problem 02 : N-Queens – Live Code in Javascript

Lecture 10: Problem 02 : NQueen – Complexity Analysis

Lecture 11: Problem 03 : Knight Tour Problem – Logic

Lecture 12: Problem 03 : Knights Tour Problem – Live Code in Javascript

Lecture 13: Problem 03 : Knight Tour Problem – Complexity Analysis

Lecture 14: Problem 04 : Boggle | Word Search – Logic

Lecture 15: Problem 04 : Boggle | Word Search – Live Code in Javascript

Lecture 16: Problem 04 : Boggle | Word Search – Complexity Analysis

Lecture 17: Section Summary

Chapter 7: Divide and Conquer

Lecture 1: Section Introduction

Lecture 2: Introduction To Divide And Conquer

Lecture 3: Identification and Approaching

Lecture 4: Problem 01 : MergeSort – Logic

Lecture 5: Problem 01 : MergeSort – Live Javascript Code

Lecture 6: Problem 01 : MergeSort – Complexity Analysis

Lecture 7: Problem 02 : QuickSort – Logic

Lecture 8: Problem 02 : QuickSort – Live Javascript Code

Lecture 9: Problem 02 : QuickSort – Complexity Analysis

Lecture 10: Problem 03 : Median Of Medians – Logic

Lecture 11: Problem 03 : Median Of Medians – Live Javascript Code

Lecture 12: Section Summary

Chapter 8: Greedy Technique

Lecture 1: Section Introduction

Lecture 2: Introduction to Greedy

Lecture 3: Identification & Approaching the Solution

Lecture 4: Problem 01 : Fractional Knapsack – Logic

Lecture 5: Problem 01 : Fractional Knapsack – Live Code Javascript

Lecture 6: Problem 01 : Fractional Knapsack – Complexity Analysis

Lecture 7: Problem 02 : IntervalScheduling – Logic

Lecture 8: Problem 02 : IntervalScheduling – Live Code Javascript

Lecture 9: Problem 02 : IntervalScheduling – Complexity Analysis

Lecture 10: Problem 03 : Huffman Code – Logic

Lecture 11: Problem 03 : Huffman Code – Live Code Javascript

Lecture 12: Problem 03 : Huffman Code – Complexity Analysis

Lecture 13: Problem 04 : Dijkstra – Logic

Lecture 14: Problem 04 : Dijkstra Logic – Live Code Javascript

Lecture 15: Problem 04 : Dijkstra – Complexity Analysis

Lecture 16: Summary

Chapter 9: Dynamic Programming

Lecture 1: Section Introduction

Lecture 2: Introduction to Dynamic Programming

Lecture 3: Identification

Lecture 4: Compare DP, D&C and Greedy

Lecture 5: Approaching the Solution

Lecture 6: Example 01 : Staircase Problem Explanation & Live Code

Lecture 7: Example 01 : Staircase Problem Complexity Analysis

Lecture 8: Example 02 – 0/1 Knapsack Explanation & Live code

Lecture 9: Example 02 – 0/1 Knapsack Complexity Analysis

Lecture 10: Example 03 – Coin Change Problem Explanation and Code

Lecture 11: Example 03 – Coin Change Problem Complexity Analysis

Lecture 12: Example 04 : Longest Decreasing Subsequence Explanation And Code

Lecture 13: Example 04 : Longest Decreasing Subsequence | Complexity Analysis

Lecture 14: Example 05 : Levenshtein problem

Instructors

Basics Strong
Team of technocrats and Programming lovers

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 13 votes

4 stars: 11 votes

5 stars: 32 votes

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