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 you?ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you?ll face as you grow your skill as a programmer.

David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problems in Swift.

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