Computer science problems that seem new or unique are
often rooted in classic algorithms, coding techniques,
and engineering principles. And classic approaches are
still the best way to solve them! Understanding these techniques in Python expands your potential for success in web
development, data munging, machine learning, and more.

Classic Computer Science Problems in Python sharpens your CS
problem-solving skills with time-tested scenarios, exercises,
and algorithms, using Python. You'll tackle dozens of coding
challenges, ranging from simple tasks like binary search algorithms to clustering data using k-means. You'll especially enjoy
the feeling of satisfaction as you crack problems that connect
computer science to the real-world concerns of apps, data,
performance, and even nailing your next job interview!

What’s Inside

• Search algorithms

• Common techniques for graphs

• Neural networks

• Genetic algorithms

• Adversarial search

• Uses type hints throughout

For intermediate Python programmers.

David Kopec is an assistant professor of Computer Science and
Innovation at Champlain College in Burlington, Vermont. He
is the author of Dart for Absolute Beginners (Apress, 2014) and
Classic Computer Science Problems in Swift (Manning, 2018).