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## Video Description

Learn how to make your Python code more efficient by using algorithms to solve a variety of tasks or computational problems. In this video course, you’ll learn algorithm basics and then tackle a series of problems using existing algorithms. The examples you’ll learn in this course are among the most common algorithms in computer science, but they illustrate many of the concerns you’ll face as you work to create algorithms on your own.

Companion files: https://github.com/heineman/python-algorithms

1. BinarySearch
1. Efficient Searching using BinaryArraySearch and Binary Search Trees Part 1 00:26:19
2. Efficient Searching using BinaryArraySearch and Binary Search Trees Part 2 00:30:10
3. Creating a Balanced Binary Search Tree from a Sorted List 00:06:22
4. An Informal Introduction to the Analysis of Algorithms 00:38:54
2. O (n log n) Behavior
1. MergeSort: A Divide and Conquer Algorithm 00:45:32
2. Using MergeSort to Sort External Data 00:11:06
3. Mathematical Algorithms
1. Mathematical Algorithms: Exponentiation By Squaring 00:37:13
2. Using Exponentiation by Squaring to Determine Whether an Integer Is Prime 00:10:25
4. Brute Force Algorithms
1. Brute Force: An Algorithm for Solving Combinatoric Problems 00:45:44
2. Using Brute Force to Generate Magic Squares 00:16:47
5. K-Dimensional Trees
1. KD Trees: Efficient Processing of Two-Dimensional Datasets Part 1 00:38:09
2. KD Trees: Efficient Processing of Two-Dimensional Datasets Part 2 00:13:45
3. Using KD Trees to Compute Nearest Neighbor Queries 00:13:56
6. Graph Algorithms
1. Graph Algorithms: Depth First Search Part 1 00:26:57
2. Graph Algorithms: Depth First Search Part 2 00:18:43
3. Using Depth First Search to Construct a Rectangular Maze 00:11:56