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Machine Learning Solutions

You're reading from   Machine Learning Solutions Expert techniques to tackle complex machine learning problems using Python

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788390040
Length 566 pages
Edition 1st Edition
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (19) Chapters Close

Machine Learning Solutions
Foreword
Contributors
Preface
1. Credit Risk Modeling 2. Stock Market Price Prediction FREE CHAPTER 3. Customer Analytics 4. Recommendation Systems for E-Commerce 5. Sentiment Analysis 6. Job Recommendation Engine 7. Text Summarization 8. Developing Chatbots 9. Building a Real-Time Object Recognition App 10. Face Recognition and Face Emotion Recognition 11. Building Gaming Bot List of Cheat Sheets Strategy for Wining Hackathons Index

Introducing the problem statement


As you know, in this chapter, we are trying to build a recommendation system. A domain that mainly uses the recommendation system is e-commerce. So, in our basic version of the recommendation engine specifically, we will be building an algorithm that can suggest the name of the products based on the category of the product. Once we know the basic concepts of the recommendation engine, we will build a recommendation engine that can suggest books in the same way as the Amazon website.

We will be building three versions of the recommendation algorithm. The baseline approach is simple but intuitive so that readers can learn what exactly the recommendation algorithm is capable of doing. Baseline is easy to implement. In the second and third approach, we will be building the book recommendation engine using ML algorithms.

Let's look at the basic methods or approaches that are used to build the recommendation system. There are two main approaches, which you can find...

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