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Applied Supervised Learning with Python

You're reading from 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

Product type Paperback

Published in Apr 2019

Publisher

ISBN-13 9781789954920

Length 404 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Ishita Mathur

Benjamin Johnston

View More author details

Table of Contents (9) Chapters

Applied Supervised Learning with Python

Preface

1. Python Machine Learning Toolkit FREE CHAPTER

2. Exploratory Data Analysis and Visualization

3. Regression Analysis

4. Classification

5. Ensemble Modeling

6. Model Evaluation

Appendix

Chapter 1. Python Machine Learning Toolkit

Note

Learning Objectives

By the end of this chapter, you will be able to:

Explain supervised machine learning and describe common examples of machine learning problems
Install and load Python libraries into your development environment for use in analysis and machine learning problems
Access and interpret the documentation of a subset of Python libraries, including the powerful pandas library
Create an IPython Jupyter notebook and use executable code cells and markdown cells to create a dynamic report
Load an external data source using pandas and use a variety of methods to search, filter, and compute descriptive statistics of the data
Clean a data source of mediocre quality and gauge the potential impact of various issues within the data source

Note

This chapter introduces supervised learning, Jupyter notebooks, and some of the most common pandas data methods.

You have been reading a chapter from

Applied Supervised Learning with Python

Published in: Apr 2019

Publisher:

ISBN-13: 9781789954920

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Authors (2)

Benjamin Johnston

Benjamin Johnston

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 Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of 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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Mathur

Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

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