Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has 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. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from 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!

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Table of Contents (9 chapters)
Applied Supervised Learning with Python
Applied Supervised Learning with Python
Preface
Preface
Free Chapter
1
Python Machine Learning Toolkit
Python Machine Learning Toolkit
Introduction
Supervised Machine Learning
Jupyter Notebooks
pandas
Data Quality Considerations
Summary
2
Exploratory Data Analysis and Visualization
Exploratory Data Analysis and Visualization
Introduction
Summary Statistics and Central Values
Missing Values
Distribution of Values
Relationships within the Data
Summary
3
Regression Analysis
Regression Analysis
Introduction
Regression and Classification Problems
Linear Regression
Multiple Linear Regression
Autoregression Models
Summary
4
Classification
Classification
Introduction
Linear Regression as a Classifier
Logistic Regression
Classification Using K-Nearest Neighbors
Classification Using Decision Trees
Summary
5
Ensemble Modeling
Ensemble Modeling
Introduction
Overfitting and Underfitting
Bagging
Boosting
Summary
6
Model Evaluation
Model Evaluation
Introduction
Evaluation Metrics
Splitting the Dataset
Performance Improvement Tactics
Summary
Appendix
Appendix
Chapter 1: Python Machine Learning Toolkit
Chapter 2: Exploratory Data Analysis and Visualization
Chapter 3: Regression Analysis
Chapter 4: Classification
Chapter 5: Ensemble Modeling
Chapter 6: Model Evaluation

Applied Supervised Learning with Python

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

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