Grokking Dynamic Programming: A Deep Dive Using Python

Polish your programming skills by learning dynamic programming using Python and various dynamic programming patterns asked in interview questions.

Intermediate

53 Lessons

25h

Certificate of Completion

Polish your programming skills by learning dynamic programming using Python and various dynamic programming patterns asked in interview questions.

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Explanations

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This course includes

133 Playgrounds

44 Challenges

This course includes

133 Playgrounds

44 Challenges

Course Overview

Some of the toughest questions in technical interviews require dynamic programming solutions. Dynamic programming (DP) is an advanced optimization technique applied to recursive solutions. However, DP is not a one-size-fits-all technique, and it requires practice to develop the ability to identify the underlying DP patterns. With a strategic approach, coding interview prep for DP problems shouldn’t take more than a few weeks. This course starts with an introduction to DP and thoroughly discusses five DP pa...Show More

TAKEAWAY SKILLS

Python

Programming Language

Prepare For Interview

Algorithms

Dynamic Programming

What You'll Learn

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

The ability to identify and apply the underlying pattern in an interview question by assessing the problem statement

Familiarity with dynamic programming techniques with hands-on practice in a setup-free coding environment

The ability to efficiently evaluate the tradeoffs between time and space complexity in different solutions

A flexible conceptual framework for solving any dynamic programming question, by connecting problem characteristics and possible solution techniques

What You'll Learn

A deep understanding of the essential patterns behind common dynamic programming interview questions—without having to drill endless problem sets

Course Content

Getting Started

In this chapter, you will learn how to navigate the course overview, target audience, and introduction to dynamic programming coding patterns.

Course Overview

Who Should Take This Course

Introduction to Dynamic Programming

0/1 Knapsack

This chapter explores how to solve problems using the 0/1 knapsack pattern, including optimization, subset, and partitioning problems.

Introduction to 0/1 Knapsack

Solving the 0/1 Knapsack Problem Target Sum Subset Sum Count of Subset Sum Partition Array Into Two Arrays to Minimize Sum Difference Minimum Number of Refueling Stops Equal Sum Subarrays Count Square Submatrices

Unbounded Knapsack

In this chapter, you will discover how to solve problems with the unbounded knapsack pattern, such as cutting, packing, and change-making problems.

Introduction to Unbounded Knapsack Solving the Unbounded Knapsack Problem Maximum Ribbon Cut Rod Cutting Minimum Coin Change Coin Change II

Recursive Numbers

This chapter covers how to use recursive number pattern, including sequences, paths, combinatorial, and traversal problems.

Introduction to Recursive Numbers Fibonacci Numbers Staircase Problem Number Factors Count Ways to Score in a Game Unique Paths to Goal Nth Tribonacci Number The Catalan Numbers House Thief Problem Minimum Jumps to Reach the End Minimum Jumps With Fee Matrix Chain Multiplication

Longest Common Substring

This chapter explores how to solve problems using the longest common substring pattern, including matching, transformation, and subsequence problems.

Introduction to Longest Common Substring Solving the Longest Common Substring Problem Longest Common Subsequence Shortest Common Supersequence Minimum Number of Deletions and Insertions Edit Distance Problem Longest Repeating Subsequence Distinct Subsequence Pattern Matching Interleaving Strings Word Break II Longest Increasing Subsequence Minimum Deletions to Make a Sequence Sorted Maximum Sum Increasing Subsequence Longest Bitonic Subsequence Longest Alternating Subsequence Building Bridges

Palindromic Subsequence

6 Lessons

This chapter shows how to solve problems using palindromic subsequence pattern, including palindromic sequences, minimal adjustment, and segmentation problems.

Conclusion

1 Lesson

In this chapter, you will learn how to consolidate your knowledge and the next steps in your DP journey.

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