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Algorithms: A Comprehensive Guide with Worked Examples and Case Studies

Stan Ward

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Published in Fundamentals Of Machine Learning For Predictive Data Analytics Second Edition: Algorithms Worked Examples And Case Studies

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Algorithms are the backbone of computer science and software development. They provide step-by-step instructions that computers can follow to solve complex problems. Understanding algorithms is essential for anyone aiming to work in the field of computer science.

This guide is designed to provide a comprehensive overview of algorithms, from basic concepts to advanced techniques. It features worked examples and case studies to help you grasp the practical applications of algorithms.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies

by John D. Kelleher

4.8 out of 5

Language	:	English
File size	:	106502 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	336 pages

Chapter 1: Algorithm Basics

This chapter introduces the fundamental concepts of algorithms, including:

* What is an algorithm? * Types of algorithms * Algorithm analysis * Time complexity * Space complexity

Chapter 2: Sorting Algorithms

Sorting algorithms are used to arrange data in a specific Free Download. This chapter covers:

* Bubble sort * Selection sort * Insertion sort * Merge sort * Quick sort

Chapter 3: Searching Algorithms

Searching algorithms are used to find a specific element in a dataset. This chapter explores:

* Linear search * Binary search * Interpolation search * Jump search

Chapter 4: Graph Algorithms

Graph algorithms are used to solve problems involving graphs, which are data structures that represent the relationships between objects. This chapter discusses:

* Breadth-first search * Depth-first search * Dijkstra's algorithm * Floyd-Warshall algorithm

Chapter 5: Advanced Algorithms

This chapter covers more advanced algorithms, including:

* Dynamic programming * Greedy algorithms * Backtracking * Divide-and-conquer algorithms

Worked Examples

Throughout the guide, worked examples are provided to illustrate the concepts and techniques discussed. These examples include:

* Sorting an array of integers using bubble sort * Searching for a specific element in a dataset using binary search * Finding the shortest path between two nodes in a graph using Dijkstra's algorithm

Case Studies

In addition to worked examples, the guide also includes case studies that demonstrate how algorithms are used in real-world applications. These case studies cover topics such as:

* Using sorting algorithms to optimize data retrieval in a database * Applying graph algorithms to route optimization in a delivery network * Employing dynamic programming to solve complex scheduling problems

This comprehensive guide provides a solid foundation in algorithms. With its clear explanations, worked examples, and case studies, it empowers readers to develop a deep understanding of algorithms and apply them effectively to solve real-world problems.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies

by John D. Kelleher

4.8 out of 5