Recursion

Recursion, available at $64.99, has an average rating of 4.65, with 44 lectures, 11 quizzes, based on 124 reviews, and has 1381 subscribers.

You will learn about Recursion Recursive process Optimizing a recursive function (with memoization and dynamic programming) Algorithmic techniques based on recursion (backtracking and divide-and-conquer) Tail recursion Breaking down a problem into subproblems of the same type This course is ideal for individuals who are Programmers or Computer science students or Engineering students or Competitive programmers or Self-learning people It is particularly useful for Programmers or Computer science students or Engineering students or Competitive programmers or Self-learning people.

Enroll now: Recursion

Summary

Title: Recursion

Price: $64.99

Average Rating: 4.65

Number of Lectures: 44

Number of Quizzes: 11

Number of Published Lectures: 44

Number of Published Quizzes: 11

Number of Curriculum Items: 55

Number of Published Curriculum Objects: 55

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Recursion

Recursive process

Optimizing a recursive function (with memoization and dynamic programming)

Algorithmic techniques based on recursion (backtracking and divide-and-conquer)

Tail recursion

Breaking down a problem into subproblems of the same type

Who Should Attend

Programmers

Computer science students

Engineering students

Competitive programmers

Self-learning people

Target Audiences

Programmers

Computer science students

Engineering students

Competitive programmers

Self-learning people

Even if the concept of recursion is simple, a lot of people struggle with it (not understanding the recursive process, not being able to figure out the base cases and recursive cases), this is why I wanted to create a course on recursion that explains it and illustrates it in detail, it also contains 11 solved and explained coding problems to practice.

And knowing recursion will also give you a new way of thinking, which is dividing the problem into multiple instances of the same problem, which will help you understanding techniques like dynamic programming, backtracking

See you in the first lecture!

The course covers:

What is recursion

Code and execution

Base cases and recursive cases

Multiple recursive calls process

Call stack

Recursion tree

How to visualize the process

Recursive functions complexity analysis (time and space comp)

Recursion vs Iteration

How to optimize a recursive function (memoization and dynamic programming)

Divide-and-conquer

Backtracking

Recursive data structures

Tail recursion

Double recursion

How to think recursively

Plus 11 solved and explained coding problems to practice:

Sum of digits

Count occurrences

Has adjacent duplicates

Reverse string

Minimum cost path in matrix

All possible phrases

Keypad combinations

String subsequences

Binary numbers with at most 2 zeros

Word search

Array permutations

Why you should take this course:

Detailed explanation of how the recursive process works

Animated examples

Good audio/video quality

Real English captions

Contains coding problems to practice

Ability to ask questions if you don’t understand something

Course Curriculum

Chapter 1: Introduction

Lecture 1: What is recursion?

Lecture 2: Code and execution

Lecture 3: Base cases and recursive cases

Chapter 2: Multiple recursive calls

Lecture 1: Process explanation

Lecture 2: Recursion tree

Chapter 3: Visualize call stack and recursion tree

Lecture 1: Visualize call stack

Lecture 2: Visualize recursion tree

Chapter 4: Time and space complexity analysis of recursive functions

Lecture 1: Recursion and time/space complexity

Lecture 2: Recursion tree method

Lecture 3: Recurrence relation method

Lecture 4: Master theorem method

Lecture 5: Space complexity of a recursive algorithm

Chapter 5: Recursion vs Iteration

Lecture 1: The comparison

Lecture 2: From recursion to iteration

Lecture 3: From iteration to recursion

Chapter 6: Optimize a recursive function with memoization and dynamic programming

Lecture 1: What is memoization

Lecture 2: Optimize ways to climb stairs solution with memoization

Lecture 3: What is dynamic programming

Lecture 4: Optimize ways to climb stairs solution with dynamic programming

Chapter 7: Divide-and-conquer and backtracking

Lecture 1: What is divide-and-conquer

Lecture 2: What is backtracking

Lecture 3: N-queens problem

Chapter 8: Recursive data structures

Lecture 1: Recursion and linked lists

Lecture 2: Recursion and trees

Lecture 3: Recursion and graphs

Chapter 9: Tail recursion

Lecture 1: What is tail recursion

Lecture 2: Examples

Chapter 10: Double recursion

Lecture 1: What is double recursion

Chapter 11: How to think recursively

Lecture 1: How to think recursively

Chapter 12: Coding problem #1: Sum of digits problem

Lecture 1: Solution + code

Chapter 13: Coding problem #2: Count occurrences

Lecture 1: Solution + code

Chapter 14: Coding problem #3: Has adjacent duplicates problem

Lecture 1: Solution + code

Chapter 15: Coding problem #4: Reverse string

Lecture 1: Solution + code

Chapter 16: Coding problem #5: Minimum cost path in matrix

Lecture 1: Solution + code

Chapter 17: Coding problem #6: All possible phrases

Lecture 1: Solution + code

Lecture 2: Important note

Chapter 18: Coding problem #7: Keypad combinations

Lecture 1: Solution + code

Lecture 2: Important note

Chapter 19: Coding problem #8: String subsequences

Lecture 1: Solution + code

Chapter 20: Coding problem #9: Binary numbers with at most 2 zeros

Lecture 1: Solution + code

Lecture 2: Important note

Chapter 21: Coding problem #10: Word search

Lecture 1: Solution + code

Chapter 22: Coding problem #11: Array permutations

Lecture 1: Solution + code

Chapter 23: Conclusion

Lecture 1: Conclusion

Instructors

Inside Code
Your algorithms and data structures content provider

Rating Distribution

1 stars: 2 votes

2 stars: 5 votes

3 stars: 5 votes

4 stars: 22 votes

5 stars: 90 votes

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