You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

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Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

The ethics of machine learning algorithms

Machine learning algorithms are increasingly used in our daily lives, and it's important we consider the ethical side of using these powerful tools.

Bias

A major way in which machine learning algorithms can cause problems has to do with bias. This bias usually shows up as disproportionately affecting a group of people and tends to fall along gender and racial divides. There are many examples to demonstrate this. For gender issues, machine learning algorithms have been seen to maltreat women. Part of this is due to the gender proportions in jobs such as software engineers, data scientists, and other similar jobs – often these have a much higher proportion of men than women overall. To start, most AI assistants such as Siri and Alexa have historically had default female voices that reinforce the stereotype of women being subservient to others (although this is changing). However, that is a design choice rather than an ML...