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:
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What is recursion
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Code and execution
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Base cases and recursive cases
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Multiple recursive calls process
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Call stack
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Recursion tree
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How to visualize the process
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Recursive functions complexity analysis (time and space comp)
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Recursion vs Iteration
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How to optimize a recursive function (memoization and dynamic programming)
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Divide-and-conquer
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Backtracking
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Recursive data structures
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Tail recursion
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Double recursion
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How to think recursively
Plus 11 solved and explained coding problems to practice:
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Sum of digits
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Count occurrences
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Has adjacent duplicates
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Reverse string
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Minimum cost path in matrix
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All possible phrases
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Keypad combinations
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String subsequences
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Binary numbers with at most 2 zeros
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Word search
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Array permutations
Why you should take this course:
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Detailed explanation of how the recursive process works
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Animated examples
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Good audio/video quality
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Real English captions
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Contains coding problems to practice
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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
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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
Frequently Asked Questions
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