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Grokking Dynamic Programming Patterns: Coding Interviews

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Grokking Dynamic Programming Patterns: Coding Interviews, available at $54.99, has an average rating of 4.6, with 132 lectures, based on 72 reviews, and has 17054 subscribers.

You will learn about Dynamic Programming Algorithms Pattern Step by step approach to solve almost any dynamic programming problem Two approaches of dynamic programming algorithms: memoization (top-down) and tabulation (bottom-up) Develop strong understanding in dynamic programming Be able to understand when to use Dynamic Programming Practice most frequently asked dynamic programming questions This course is ideal for individuals who are Anyone who wants to master the art of dynamic programming or Anyone who fear dynamic programming algorithms or Anybody who want to understand dynamic programming algorithms or Anyone who want to be prepared for coding interview at MAANG Company or Computer science students, self taught programmers etc or competitive programmers It is particularly useful for Anyone who wants to master the art of dynamic programming or Anyone who fear dynamic programming algorithms or Anybody who want to understand dynamic programming algorithms or Anyone who want to be prepared for coding interview at MAANG Company or Computer science students, self taught programmers etc or competitive programmers.

Enroll now: Grokking Dynamic Programming Patterns: Coding Interviews

Summary

Title: Grokking Dynamic Programming Patterns: Coding Interviews

Price: $54.99

Average Rating: 4.6

Number of Lectures: 132

Number of Published Lectures: 132

Number of Curriculum Items: 132

Number of Published Curriculum Objects: 132

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

  • Dynamic Programming Algorithms Pattern
  • Step by step approach to solve almost any dynamic programming problem
  • Two approaches of dynamic programming algorithms: memoization (top-down) and tabulation (bottom-up)
  • Develop strong understanding in dynamic programming
  • Be able to understand when to use Dynamic Programming
  • Practice most frequently asked dynamic programming questions

Who Should Attend

  • Anyone who wants to master the art of dynamic programming
  • Anyone who fear dynamic programming algorithms
  • Anybody who want to understand dynamic programming algorithms
  • Anyone who want to be prepared for coding interview at MAANG Company
  • Computer science students, self taught programmers etc or competitive programmers

Target Audiences

  • Anyone who wants to master the art of dynamic programming
  • Anyone who fear dynamic programming algorithms
  • Anybody who want to understand dynamic programming algorithms
  • Anyone who want to be prepared for coding interview at MAANG Company
  • Computer science students, self taught programmers etc or competitive programmers

Welcome to this course – “Dynamic Programming Algorithms for Coding Interviews”.

This course on Dynamic Programming Coding Interview Algorithms will teach you the advanced algorithms and data structures needed for coding interviews and technical interviews. You’ll learn how to solve dynamic programming questions, and you’ll master the fundamentals of data structures and algorithms. You’ll also get an in-depth understanding of Grokking Dynamic Programming Interview Patterns for Technical Interviews, and you’ll learn the skills needed to solve the toughest coding interview questions. Finally, you’ll get hands-on experience with Java Dynamic Programming questions and Algorithms for Coding Interviews, and you’ll Master Dynamic Programming Coding Interview Algorithms and ace your next job interview. This course will teach you the fundamentals of dynamic programming and how to use them to solve complex coding interview questions quickly and confidently. You will learn the fundamentals of data structures and algorithms, as well as how to apply them to coding interview questions. You will also learn to use Java and dynamic programming techniques to solve dynamic programming questions related to Google, LeetCode, and other technical interviews. You will also learn the best practices for mastering the coding interview data structures and algorithms, as well as how to review and apply them in the real world.

Are you struggling with DP Problem?

If you often struggle with dynamic programming problems despite your understanding of data structures and algorithms, this course is designed to bridge that gap. It provides a comprehensive understanding of critical Dynamic Programming concepts, empowering you to excel in competitive coding and interviews.

In addition to the mentioned problems, the “Dynamic Programming Algorithms  Coding Interviews” course covers several more essential dynamic programming problems. Through detailed explanations, code implementations, and step-by-step walkthroughs, you’ll gain a deep understanding of each problem’s solution.

We have 30 day money back, guarantee, enrol now, see you inside 🙂

Course Feedback by Student:

Asif Khondokar

Amazing course. This is the best Dynamic Programming Course on Udemy. Thanks for creating this course Md. A. Barik. I got job offer & you help me a lot. Thanks again

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Revise Code

Chapter 2: ##### Pattern 1# [0/1 Knapsack Pattern]

Lecture 1: Pattern 1 — Problems

Chapter 3: 0/1 Knapsack

Lecture 1: 0/1 Knapsack Problem – Top Down

Lecture 2: 0/1 Knapsack Problem – Bottom UP (2D Tabulation)

Lecture 3: 0/1 Knapsack Problem – Bottom UP (1D Tabulation)

Chapter 4: Target Sum

Lecture 1: Problem Statement

Lecture 2: Target Sum – Top Down

Lecture 3: Target Sum – Bottom UP

Chapter 5: Count of Subset Sum

Lecture 1: Problem Statement

Lecture 2: Count of Subset Sum – Top Down

Lecture 3: Count of Subset Sum – Bottom Up [2D Tabulation]

Lecture 4: Count of Subset Sum – Bottom UP [1D Tabulation]

Chapter 6: Minimum Sum Partition

Lecture 1: Problem Statement

Lecture 2: Minimum Sum Partition – Top Down

Lecture 3: Minimum Sum Partition – Bottom UP [1D Tabulation]

Chapter 7: Minimum Number of Refueling Stops

Lecture 1: Problem Statement

Lecture 2: Minimum Number of Refuelling Stops – Top Down

Lecture 3: Minimum Number of Refuelling Stops – Bottom UP [1D Tabulation]

Chapter 8: Partition Equal Subset Sum

Lecture 1: Problem Statement

Lecture 2: Partition Equal Subset Sum – Top Down

Lecture 3: Partition Equal Subset Sum – Bottom UP [1D Tabulation]

Chapter 9: Count Square Submatrices with All Ones

Lecture 1: Problem Statement

Lecture 2: Count Square Submatrices with All Ones – Top Down

Lecture 3: Count Square Submatrices with All Ones – Bottom Up

Chapter 10: ##### Pattern 2# [Unbounded Knapsack Pattern]

Lecture 1: Pattern 2 — Problems

Chapter 11: Unbounded Knapsack

Lecture 1: Problem Statement

Lecture 2: Unbounded Knapsack – Top Down

Lecture 3: Unbounded Knapsack – Bottom UP [2D Tabulation]

Lecture 4: Unbounded Knapsack – Bottom UP [1D Tabulation]

Chapter 12: Maximum Ribbon Cut

Lecture 1: Problem Statement

Lecture 2: Maximum Ribbon Cut – Top Down

Lecture 3: Maximum Ribbon Cut – Bottom UP [2D Tabulation]

Chapter 13: Rod Cutting

Lecture 1: Problem Statement

Lecture 2: Rod Cutting – Top Down

Lecture 3: Rod Cutting – Bottom UP [2D Tabulation]

Chapter 14: Coin Change

Lecture 1: Problem Statement

Lecture 2: Coin Change – Top down

Lecture 3: Coin Change – Bottom UP [1D Tabulation]

Chapter 15: Coin Change II

Lecture 1: Problem Statement

Lecture 2: Coin Change II – Top Down

Lecture 3: Coin Change II – Bottom UP [2D Tabulation]

Lecture 4: Coin Change II – Bottom UP [1D Tabulation]

Chapter 16: ##### Pattern #3 [Recursive Number Pattern]

Lecture 1: Pattern 3 — Problems

Chapter 17: Fibonacci Number

Lecture 1: Problem Statement

Lecture 2: Fibonacci Number – Top Down

Lecture 3: Fibonacci Number – Bottom UP [1D Tabulation]

Lecture 4: Fibonacci Number – Bottom UP [Constant Space]

Chapter 18: Climbing Stairs

Lecture 1: Problem Statement

Lecture 2: Climbing Stairs – Top Down

Lecture 3: Climbing Stairs – Bottom UP

Chapter 19: Decode Ways

Lecture 1: Problem Statement

Lecture 2: Decode Ways – Top Down

Lecture 3: Decode Ways – Bottom UP [1D Tabulation]

Lecture 4: Decode Ways – Bottom UP [Space Optimized]

Chapter 20: House Robber

Lecture 1: Problem Statement

Lecture 2: House Robber – Top Down

Lecture 3: House Robber – Bottom UP

Chapter 21: Number Factor

Lecture 1: Problem Statement

Lecture 2: Number Factors – Top Down

Lecture 3: Number Factors – Bottom UP

Chapter 22: Count Ways to Score in a Game

Lecture 1: Problem Statement

Lecture 2: Count Ways to Score in a Game – Top Down

Lecture 3: Count Ways to Score in a Game – Bottom UP

Chapter 23: Unique Paths to Goal

Lecture 1: Problem Statement

Lecture 2: Unique Paths to Goal – Top Down

Lecture 3: Unique Paths to Goal – Bottom UP

Chapter 24: Nth Tribonacci Number

Lecture 1: Problem Statement

Lecture 2: Nth Tribonacci Number – Top Down

Lecture 3: Nth Tribonacci Number – Bottom UP

Chapter 25: The Catalan Numbers

Lecture 1: Problem Statement

Lecture 2: The Catalan Numbers – Top Down

Lecture 3: The Catalan Numbers – Bottom UP

Chapter 26: Minimum Jumps to Reach the End

Lecture 1: Problem Statement

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 6 votes
  • 4 stars: 19 votes
  • 5 stars: 46 votes

Frequently Asked Questions


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