Benjamin Johnston - Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
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- Book:Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
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Explore the exciting world of machine learning with the fastest growing technology in the world
Key Features- Understand various machine learning concepts with real-world examples
- Implement a supervised machine learning pipeline from data ingestion to validation
- Gain insights into how you can use machine learning in everyday life
Machine learningthe ability of a machine to give right answers based on input datahas revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. Youll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.
With the help of fun examples, youll gain experience working on the Python machine learning toolkitfrom performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once youve grasped the basics, youll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. Youll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.
This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.
By the end of this book, youll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
What you will learn- Understand the concept of supervised learning and its applications
- Implement common supervised learning algorithms using machine learning Python libraries
- Validate models using the k-fold technique
- Build your models with decision trees to get results effortlessly
- Use ensemble modeling techniques to improve the performance of your model
- Apply a variety of metrics to compare machine learning models
Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. Itll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
Table of Contents- Python Machine Learning Toolkit
- Exploratory Data Analysis and Visualization
- Regression Analysis
- Classification
- Ensemble Modeling
- Model Evaluation
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Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning
Benjamin Johnston and Ishita Mathur
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 author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be 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.
Authors: Benjamin Johnston and Ishita Mathur
Reviewer: Priyanka Das
Managing Editor: Taabish Khan
Acquisitions Editors: Aditya Date and Kunal Sawant
Production Editor: Nitesh Thakur
Editorial Board: David Barnes, Ewan Buckingham, Simon Cox, Manasa Kumar, Alex Mazonowicz, Douglas Paterson, Dominic Pereira, Shiny Poojary, Erol Staveley, Ankita Thakur, and Mohita Vyas.
First Published: April 2019
Production Reference: 1250419
ISBN: 978-1-78995-492-0
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
This section briefly introduces the authors, what this book covers, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.
Machine learningthe ability of a machine to give correct answers based on input datahas revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You'll explore Jupyter notebooks, a technology that's widely used in academic and commercial circles with support for running inline code.
With the help of fun examples, you'll gain experience working on the Python machine learning toolkitfrom performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you've grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.
This book also covers ensemble modeling and random forest classifiers, along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.
By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Benjamin Johnston is a senior data scientist for one of the world's leading data-driven medtech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition, to solution research and development, through to final deployment. He is currently completing his PhD in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years' experience in medical device design and development, working in a variety of technical roles and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.
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