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Alexander Combs - Python Machine Learning Blueprints: Put your machine learning concepts to the test by developing real-world smart projects

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Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and KerasMachine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, youll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, youll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, youll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, youll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and youll even create an application using computer vision and neural networks.By the end of this book, youll be able to analyze data seamlessly and make a powerful impact through your projects.What you will learn Understand the Python data science stack and commonly used algorithms Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window Understand NLP concepts by creating a custom news feed Create applications that will recommend GitHub repositories based on ones youve starred, watched, or forked Gain the skills to build a chatbot from scratch using PySpark Develop a market-prediction app using stock data Delve into advanced concepts such as computer vision, neural networks, and deep learning

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Python Machine Learning Blueprints Second Edition Put your machine - photo 1
Python Machine Learning Blueprints
Second Edition
Put your machine learning concepts to the test by developing real-world smart projects
Alexander Combs
Michael Roman

BIRMINGHAM - MUMBAI Python Machine Learning BlueprintsSecond Edition - photo 2

BIRMINGHAM - MUMBAI
Python Machine Learning BlueprintsSecond Edition

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Sunith Shetty
Acquisition Editor: Varsha Shetty
Content Development Editor: Snehal Kolte
Technical Editor: Naveen Sharma
Copy Editor: Safis Editing
Project Coordinator: Manthan Patel
Proofreader: Safis Editing
Indexer: Mariammal Chettiyar
Graphics: Jisha Chirayil
Production Coordinator: Arvindkumar Gupta

First published: July 2016
Second edition: January 2019

Production reference: 1310119

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78899-417-0

www.packtpub.com

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Contributors
About the authors

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 .

Writing a book is truly a massive undertaking that would not be possible without the support of others. I would like to thank my family for their love and encouragement and Jocelyn for her patience and understanding. I owe all of you tremendously .

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.

About the reviewer

Saurabh Chhajed is a machine learning and big data engineer with 9 years of professional experience in the enterprise application development life cycle using the latest frameworks, tools, and design patterns. He has experience of designing and implementing some of the most widely used and scalable customer-facing recommendation systems with extensive usage of the big data ecosystem the batch, real-time, and machine learning pipeline. He has also worked for some of the largest investment banks, credit card companies, and manufacturing companies around the world, implementing a range of robust and scalable product suites.

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Preface

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover the key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.

The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll learn how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects in domains such as predictive analytics to analyze the stock market, and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and even create an application using computer vision and neural networks.

By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects.

Who this book is for

This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. This intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains.

What this book covers

, The Python Machine Learning Ecosystem

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