Cover image for Working with Algorithms in Python

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.

Table of Contents

  1. BinarySearch The online content for Working with Algorithms in Python can be found here
    1. Efficient Searching using BinaryArraySearch and Binary Search Trees Part 1
      00:26:18
    2. Efficient Searching using BinaryArraySearch and Binary Search Trees Part 2
      00:30:09
    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
      00:11:05
  3. Mathematical Algorithms
    1. Mathematical Algorithms: Exponentiation By Squaring
      00:37:13
    2. Using Exponentiation by Squaring to Determine Whether an Integer Is Prime
      00:10:24
  4. Brute Force Algorithms
    1. Brute Force: An Algorithm for Solving Combinatoric Problems
      00:45:44
    2. Using Brute Force to Generate Magic Squares
      00:16:46
  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
      00:26:56
    2. Graph Algorithms: Depth First Search Part 2
      00:18:43
    3. Using Depth First Search to Construct a Rectangular Maze
      00:11:55
  7. AllPairsShortestPath
    1. Graph Algorithms: All Pairs Shortest Path
      00:40:48
    2. Using Dynamic Programming to Compute Minimum Edit Distance
      00:08:04
  8. Heap Data Structure
    1. The Heap Data Structure and Its Use in HeapSort
      00:26:52
    2. Using HeapSort to Sort a Collection
      00:07:38
  9. Single-Source Shortest Path
    1. Single-Source Shortest Path: Using Priority Queues
      00:35:23
    2. Using Priority Queues to Compute the Minimum Spanning Tree
      00:07:10
  10. Summary
    1. Course Summary
      00:01:56