Book Image

Hands-On Data Structures and Algorithms with Python - Second Edition

By : Dr. Basant Agarwal, Benjamin Baka
Book Image

Hands-On Data Structures and Algorithms with Python - Second Edition

By: Dr. Basant Agarwal, Benjamin Baka

Overview of this book

Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.
Table of Contents (16 chapters)

Summary

In this chapter, we have explored how data and algorithms come together to aid machine learning. Making sense of huge amounts of data is made possible by first pruning our data through data cleaning techniques and scaling and normalization processes. Feeding this data to specialized learning algorithms, we are able to predict the categories of unseen data based on the patterns learned by the algorithm from the data. We also discussed the basics of machine learning algorithms.

We explained supervised and unsupervised machine learning algorithms in detail with the naive bayes and k-means clustering algorithms. We also provided the implementation of these algorithms using the scikit-learn Python-based machine learning library. Finally, some important visualization techniques were discussed, as charting and plotting the condensed data helps you to better understand and make...