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You're reading from Python Machine Learning Blueprints Put your machine learning concepts to the test by developing real-world smart projects

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781788994170

Length 378 pages

Edition 2nd Edition

Languages

Python

Tools

GitHub

Concepts

Machine Learning

Authors (3):

Michael Roman

Alexander Combs

Saurabh Chhajed

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Table of Contents (13) Chapters

Preface

1. The Python Machine Learning Ecosystem FREE CHAPTER

2. Build an App to Find Underpriced Apartments

3. Build an App to Find Cheap Airfares

4. Forecast the IPO Market Using Logistic Regression

5. Create a Custom Newsfeed

6. Predict whether Your Content Will Go Viral

7. Use Machine Learning to Forecast the Stock Market

8. Classifying Images with Convolutional Neural Networks

9. Building a Chatbot

10. Build a Recommendation Engine

11. What's Next?

12. Other Books You May Enjoy

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Study feature selection and the feature engineering process
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Authors (3)

Chhajed

Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at http://saurzcode.in. Saurabh has also reviewed books by Packt Publishing, Apache Camel Essentials and Java EE 7 Development with NetBeans 8, in the past.

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Combs

Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City.

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Roman

Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.

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