To extract useful information from the given data, we initially collect the raw data that is to be used to learn the patterns. Next, we apply the data preprocessing techniques to remove the noise from the data. Further more, we extract the important features from the data, which are representative of the data, to develop the model. Feature extraction is the most crucial step for machine learning algorithms to work effectively. A good feature must be informative and discriminating for the machine learning algorithms. Feature selection techniques are used to remove the irrelevant, redundant, and noisy features. Further more, the prominent features are fed to the machine learning algorithms to learn the patterns in the data. Finally, we apply the evaluation measure to judge the performance of the developed model and use visualization techniques to visualize...
Hands-On Data Structures and Algorithms with Python
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Hands-On Data Structures and Algorithms with Python
By:
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)
Preface
Free Chapter
Python Objects, Types, and Expressions
Python Data Types and Structures
Principles of Algorithm Design
Lists and Pointer Structures
Stacks and Queues
Trees
Hashing and Symbol Tables
Graphs and Other Algorithms
Searching
Sorting
Selection Algorithms
String Algorithms and Techniques
Design Techniques and Strategies
Implementations, Applications, and Tools
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