Classic Computer Science Problems in Python

Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means.   Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems   Key Features   Breadth-first and depth-first search algorithms   Constraints satisfaction problems   Common techniques for graphs   Adversarial Search   Neural networks and genetic algorithms   Written for data engineers and scientists with experience using Python.   For readers comfortable with the basics of Python   About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and youll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges youll face as you grow your skill as a programmer.   David Kopec teaches at Champlain College in Burlington, VT and is the author of Mannings Classic Computer Science Problemsin Swift.