Algorithms in Python - Design Techniques And Approach

Algorithms in Python : Design Techniques And Approach, available at $59.99, has an average rating of 4.65, with 158 lectures, based on 19 reviews, and has 356 subscribers.

You will learn about Algorithms In Python Algorithms Design Techniques Data Structures Tress Graphs Dynamic Programming , Recursion, Backtracking This course is ideal for individuals who are Want to Learn to Design Algorithms It is particularly useful for Want to Learn to Design Algorithms.

Enroll now: Algorithms in Python : Design Techniques And Approach

Summary

Title: Algorithms in Python : Design Techniques And Approach

Price: $59.99

Average Rating: 4.65

Number of Lectures: 158

Number of Published Lectures: 158

Number of Curriculum Items: 158

Number of Published Curriculum Objects: 158

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Algorithms In Python

Algorithms Design Techniques

Data Structures

Tress

Graphs

Dynamic Programming , Recursion, Backtracking

Who Should Attend

Want to Learn to Design Algorithms

Target Audiences

Want to Learn to Design Algorithms

Algorithm Design Techniques: Live problem-solving in Python

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

Completer scientists have worked for 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

6. Trees

7. Graphs

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

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: 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 – part 1

Lecture 3: Complexity Analysis – part 2

Lecture 4: Summary

Chapter 4: Recurrence Relation

Lecture 1: Section Introduction

Lecture 2: Recurrence Relation

Lecture 3: Solving Recurrence Relation

Lecture 4: Masters Theorem

Lecture 5: Summary

Chapter 5: Thinking Recursively

Lecture 1: Section Introduction

Lecture 2: Recursion

Lecture 3: Identification

Lecture 4: Approaching the solution

Lecture 5: Problem 01 : FindingSubstrings – Logic

Lecture 6: Problem 01 : FindingSubstrings – Live Code Python

Lecture 7: Problem 01 : FindingSubstrings – Complexity Analysis

Lecture 8: Problem 02 : Tower Of Hanoi

Lecture 9: Problem 02 : Tower of Hanoi – Lets code in Python

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 Python

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 : NQueen – Logic

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

Lecture 10: Problem 02 : N-Queens – Complexity Analysis

Lecture 11: Problem 03 : Knights Tour Problem – Logic

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

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 Python

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 Python Code

Lecture 6: Problem 01 : MergeSort – Complexity Analysis

Lecture 7: Problem 02 : QuickSort – Logic

Lecture 8: Problem 02 : QuickSort – Live Python Code

Lecture 9: Problem 02 : QuickSort – Complexity Analysis

Lecture 10: Problem 03 : Median Of Medians – Logic

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

Lecture 6: Problem 01 : Fractional Knapsack – Complexity Analysis

Lecture 7: Problem 02 : IntervalScheduling – Logic

Lecture 8: Problem 02 : IntervalScheduling – Live Code Python

Lecture 9: Problem 02 : IntervalScheduling – Complexity Analysis

Lecture 10: Problem 03 : Huffman Code – Logic

Lecture 11: Problem 03 : Huffman Code – Live Code

Lecture 12: Problem 03 : Huffman Code – Complexity Analysis

Lecture 13: Problem 04 : Dijkstra – Logic

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

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 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

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3 stars: 2 votes

4 stars: 7 votes

5 stars: 10 votes

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