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Machine Learning Fundamentals

You're reading from Machine Learning Fundamentals Use Python and scikit-learn to get up and running with the hottest developments in machine learning

Product type Paperback

Published in Nov 2018

Publisher

ISBN-13 9781789803556

Length 240 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Hyatt Saleh

View More author details

Table of Contents (8) Chapters

Preface

About the Book

1. Introduction to Scikit-Learn FREE CHAPTER

2. Unsupervised Learning: Real-Life Applications

3. Supervised Learning: Key Steps

4. Supervised Learning Algorithms: Predict Annual Income

5. Artificial Neural Networks: Predict Annual Income

6. Building Your Own Program

Appendix

Introduction

In the previous chapter, we saw how to represent data in a tabular format, create features and target matrices, preprocess data, and choose the algorithm that best suits the problem at hand. We also saw how the scikit-learn API works and why it is easy to use.

The main objective of this chapter is to solve a real-world case study, where the students will implement three different unsupervised learning solutions. These different applications serve to demonstrate the uniformity of the scikit-learn API, as well as to explain the steps taken to solve such a problem. At the end of this chapter, the students will be able to understand the use of unsupervised learning to comprehend data in order to make informed decisions.

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Authors (1)

Hyatt Saleh

Hyatt Saleh

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.

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