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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning 2. Parallelism of Statistics and Machine Learning FREE CHAPTER 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

Recommendation Engines

Recommendation engines (REs) are most famously used for explaining what machine learning is to any unknown person or a newbie who wants to understand the field of machine learning. A classic example could be how Amazon recommends books similar to the ones you have bought, which you may also like very much! Also, empirically, the recommender engine seems to be an example of large-scale machine learning that everyone understands, or perhaps already understood. But, nonetheless, recommendation engines are being used everywhere. For example, the people you may know feature in Facebook or LinkedIn, which recommends by showing the most probable people you might like to befriend or professionals in your network who you might be interested in connecting with. Of course, these features drive their businesses big time and it is at the heart of the company's driving...

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