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The Machine Learning Workshop

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh , John Wesley Doyle , Akshat Gupta, Harshil Jain, Vikraman Karunanidhi, Subhojit Mukherjee, Madhav Pandya, Subhash Sundaravadivelu
4.3 (6)
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The Machine Learning Workshop

The Machine Learning Workshop

4.3 (6)
By: Hyatt Saleh , John Wesley Doyle , Akshat Gupta, Harshil Jain, Vikraman Karunanidhi, Subhojit Mukherjee, Madhav Pandya, Subhash Sundaravadivelu

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)
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Preface

Introduction

In the previous chapter, we learned how to represent data in a tabular format, created features and target matrices, pre-processed data, and learned how to choose the algorithm that best suits the problem at hand. We also learned how the scikit-learn API works and why it is easy to use, as well as the difference between supervised and unsupervised learning.

This chapter focuses on the most important task in the field of unsupervised learning: clustering. Consider a situation in which you are a store owner wanting to make a targeted social media campaign to promote selected products to certain customers. Using clustering algorithms, you would be able to create subgroups of your customers, allowing you to profile those subgroups and target them accordingly. The main objective of this chapter is to solve a case study, where you 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 machine learning problems. By the end of this chapter, you will be able to understand the use of unsupervised learning to comprehend data in order to make informed decisions.

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The Machine Learning Workshop
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