Book Image

40 Algorithms Every Programmer Should Know

By : Imran Ahmad
5 (2)
Book Image

40 Algorithms Every Programmer Should Know

5 (2)
By: Imran Ahmad

Overview of this book

Algorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.
Table of Contents (19 chapters)
1
Section 1: Fundamentals and Core Algorithms
7
Section 2: Machine Learning Algorithms
13
Section 3: Advanced Topics

Summary

In this chapter, we learned about graph-based algorithms. After going through this chapter, I expect that we should be able to use different techniques of representing, searching, and processing data represented as graphs. We also developed skills to be able to calculate the shortest distance between two vertices and we built neighborhoods in our problem space. This knowledge should help us use graph theory to address problems such as fraud detection.

In the next chapter, we will focus on different unsupervised machine learning algorithms. Many of the use-case techniques discussed in this chapter complement unsupervised learning algorithms, which will be discussed in detail in the next chapter. Finding evidence of fraud in a dataset is an example of such use cases.