Gain insights into dynamic programming, explore recursion basics, and delve into advanced techniques like Bottom-Up optimization. Discover ways to solve complex problems more efficiently with hands-on coding challenges.

Dynamic programming is something every developer should have in their toolkit. It allows you to optimize your algorithm with respect to time and space — a very important concept in real-world applications.

In this course, you’ll start by learning the basics of recursion and work your way to more advanced DP concepts like Bottom-Up optimization. Throughout this course, you will learn various types of DP techniques for solving even the most complex problems. Each section is complete with coding challenges of varying difficulty so you can practice a wide range of problems.

By the time you’ve completed this course, you will be able to utilize dynamic programming in your own projects.

Dynamic Programming in Python: Optimizing Programs for Efficiency

What edge does bottom-up dynamic programming have over top-down dynamic programming?

What edge does bottom-up dynamic programming have over top-down dynamic programming?

By using tabulation instead of memoization, we can improve our algorithm's space complexity.

By using tabulation instead of memoization, we can improve our algorithm’s space complexity.

Similar to Fibonacci numbers, space complexity of Catalan's numbers can be reduced to **O(1)**.

Similar to Fibonacci numbers, space complexity of Catalan’s numbers can be reduced to O(1).

We cannot reduce the space complexity of the Fibonacci numbers algorithm to **O(1)** if we use the top-down approach.

We cannot reduce the space complexity of the Fibonacci numbers algorithm to O(1) if we use the top-down approach.

**O(min(length of str1, length of str2))**

**O(max(length of str1, length of str2))**

We have two strings, `str1` and `str2`, of varied sizes. What would be the space complexity of an efficient algorithm to compute the length of the longest common substring?


We have two strings, <code>str1</code> and <code>str2</code>, of varied sizes. What would be the space complexity of an efficient algorithm to compute the length of the longest common substring?

In this lesson, you will be quizzed on your knowledge of bottom-up dynamic programming

Chapter 1: From Recursion to Dynamic Programming

Chapter 2: Top-Down Dynamic Programming with Memoization

Chapter 3: Bottom-Up Dynamic Programming with Tabulation

Chapter 4: Practice Problems

Quiz