Algorithms in Java -Live problem solving Design Techniques

Algorithms in Java :Live problem solving & Design Techniques, available at $74.99, has an average rating of 4.7, with 167 lectures, based on 160 reviews, and has 2252 subscribers.

You will learn about Algorithm Design in Computer Systems Algorithm Design Techniques How to Code a Algo in Java Calculate time and space complexity Dynamic Programming, Greedy, D&C and Much more This course is ideal for individuals who are Looking to learn to design algorithms or Want to develop how to think solve algo problems or Code Famous Algorithms in Java It is particularly useful for Looking to learn to design algorithms or Want to develop how to think solve algo problems or Code Famous Algorithms in Java.

Enroll now: Algorithms in Java :Live problem solving & Design Techniques

Summary

Title: Algorithms in Java :Live problem solving & Design Techniques

Price: $74.99

Average Rating: 4.7

Number of Lectures: 167

Number of Published Lectures: 167

Number of Curriculum Items: 167

Number of Published Curriculum Objects: 167

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Algorithm Design in Computer Systems

Algorithm Design Techniques

How to Code a Algo in Java

Calculate time and space complexity

Dynamic Programming, Greedy, D&C and Much more

Who Should Attend

Looking to learn to design algorithms

Want to develop how to think solve algo problems

Code Famous Algorithms in Java

Target Audiences

Looking to learn to design algorithms

Want to develop how to think solve algo problems

Code Famous Algorithms in Java

Algorithm Design Techniques: Live problem-solving in Java

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

Completer scientists have worked from 100s of years 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.

0. Complexity analysis

1. Recursion is the base of any algorithm design

2. Backtracking

3. Divide and Conquer

4. Greedy algorithms

5. Dynamic programming

And WE WILL WRITE THE CODE LINE BY LINE IN JAVA !!

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.

THRILLED? 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.

Happy Learning

Basics>Strong;

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Course 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 : FindingSubsequences – Logic

Lecture 6: Problem 01 : FindingSubsequences – Live Code Java

Lecture 7: Problem 01 : FindingSubsequences – Complexity Analysis

Lecture 8: Problem 02: Tower of Hanoi – Logic

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

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

Lecture 11: Problem 03 : 7.Array Product Sum – Logic

Lecture 12: Problem 03 : 7.Array Product Sum – Live Code Java

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

Lecture 14: Problem 04 : Binary Subtree – Logic

Lecture 15: Problem 04 : Binary Subtree – Live Code Java

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: Section Summary

Chapter 6: Backtracking

Lecture 1: Section Introduction

Lecture 2: Introduction to Backtracking

Lecture 3: Identify Backtracking

Lecture 4: Approching 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 : NQueen – Logic

Lecture 9: Problem 02 : NQueen – Live Code in Java

Lecture 10: Problem 02 : NQueen – Complexity Analysis

Lecture 11: Problem 03 : Knights Tour Problem – Logic

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

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 Java

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 Java Code

Lecture 6: Problem 01 : MergeSort – Complexity Analysis

Lecture 7: Problem 02 : QuickSort – Logic

Lecture 8: Problem 02 : QuickSort – Live Java Code

Lecture 9: Problem 02 : QuickSort – Complexity Analysis

Lecture 10: Problem 03 : Median Of Medians – Logic

Lecture 11: Problem 03 : Median Of Medians – Live Java 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 Java

Lecture 6: Problem 01 : Fractional Knapsack – Complexity Analysis

Lecture 7: Problem 02 : IntervalScheduling – Logic

Lecture 8: Problem 02 : IntervalScheduling – Live Code Java

Lecture 9: Problem 02 : IntervalScheduling – Complexity Analysis

Lecture 10: Problem 03 : Huffman Code – Logic

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

Lecture 12: Problem 03 : Huffman Code – Complexity Analysis

Lecture 13: Problem 04 : Dijkstra – Logic

Lecture 14: Problem 04 : Dijkstra Logic – Live Code

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 Theory & Live Code

Lecture 7: Example 01 : Staircase Problem Complexity Analysis

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

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

Lecture 10: Example 03 – Coin Change Problem Theory 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: 6 votes

2 stars: 10 votes

3 stars: 13 votes

4 stars: 50 votes

5 stars: 81 votes

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