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

## Simple recursion

Following is the simple implementation of the Fibonacci numbers algorithm. We covered this in one of the [lessons](https://www.educative.io/courses/dynamic-programming-in-python/the-fibonacci-numbers) from the first chapter. Please feel free to look at it before we continue to the bottom-up solution.

# Simple recursion

Following is the simple implementation of the Fibonacci numbers algorithm. We covered this in one of the [lessons](https://www.educative.io/courses/dynamic-programming-in-python/the-fibonacci-numbers) from the first chapter. Please feel free to look at it before we continue to the bottom-up solution.

In this lesson, we will revisit the Fibonacci numbers algorithm and implement it in a bottom-up manner.

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

The Fibonacci Numbers Algorithm with Tabulation

Simple recursion