2014
O'Reilly Media
George T. Heineman
8h 39m
English

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—such as determining the shortest path through a graph and the minimum edit distance between two genomic sequences—using existing algorithms.

1. BinarySearch
Efficient Searching using BinaryArraySearch and Binary Search Trees Part 1
Efficient Searching using BinaryArraySearch and Binary Search Trees Part 2
Creating a Balanced Binary Search Tree from a Sorted List
An Informal Introduction to the Analysis of Algorithms
2. O (n log n) Behavior
MergeSort: A Divide and Conquer Algorithm
Using MergeSort to Sort External Data
3. Mathematical Algorithms
Mathematical Algorithms: Exponentiation By Squaring
Using Exponentiation by Squaring to Determine Whether an Integer Is Prime
4. Brute Force Algorithms
Brute Force: An Algorithm for Solving Combinatoric Problems
Using Brute Force to Generate Magic Squares
5. K-Dimensional Trees
KD Trees: Efficient Processing of Two-Dimensional Datasets Part 1
KD Trees: Efficient Processing of Two-Dimensional Datasets Part 2
Using KD Trees to Compute Nearest Neighbor Queries
6. Graph Algorithms
Graph Algorithms: Depth First Search Part 1
Graph Algorithms: Depth First Search Part 2
Using Depth First Search to Construct a Rectangular Maze
7. AllPairsShortestPath
Graph Algorithms: All Pairs Shortest Path
Using Dynamic Programming to Compute Minimum Edit Distance
8. Heap Data Structure
The Heap Data Structure and Its Use in HeapSort
Using HeapSort to Sort a Collection
9. Single-Source Shortest Path
Single-Source Shortest Path: Using Priority Queues
Using Priority Queues to Compute the Minimum Spanning Tree
10. Summary
Course Summary

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