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Data Science with Python

You're reading from Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

Product type Paperback

Published in Jul 2019

Publisher Packt

ISBN-13 9781838552862

Length 426 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

Data Science

Authors (3):

Rohan Chopra

Mohamed Noordeen Alaudeen

Aaron England

View More author details

Table of Contents (18) Chapters

Preface

About the Book

1. Chapter 1 FREE CHAPTER

2. Introduction to Data Science and Data Pre-Processing

3. Chapter 2

4. Data Visualization

5. Chapter 3

6. Introduction to Machine Learning via Scikit-Learn

7. Chapter 4

8. Dimensionality Reduction and Unsupervised Learning

9. Chapter 5

10. Mastering Structured Data

11. Chapter 6

12. Decoding Images

13. Chapter 7

14. Processing Human Language

15. Chapter 8

16. Tips and Tricks of the Trade

Appendix

Data Transformation

Previously, we saw how we can combine data from different sources into a unified dataframe. Now, we have a lot of columns that have different types of data. Our goal is to transform the data into a machine-learning-digestible format. All machine learning algorithms are based on mathematics. So, we need to convert all the columns into numerical format. Before that, let's see all the different types of data we have.

Taking a broader perspective, data is classified into numerical and categorical data:

Numerical: As the name suggests, this is numeric data that is quantifiable.
Categorical: The data is a string or non-numeric data that is qualitative in nature.

Numerical data is further divided into the following:

Discrete: To explain in simple terms, any numerical data that is countable is called discrete, for example, the number of people in a family or the number of students in a class. Discrete data can only take certain values...

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Authors (3)

Rohan Chopra

Rohan Chopra

Rohan Chopra graduated from Vellore Institute of Technology with a bachelors degree in computer science. Rohan has an experience of more than 2 years in designing, implementing, and optimizing end-to-end deep neural network systems. His research is centered around the use of deep learning to solve computer vision-related problems and has hands-on experience working on self-driving cars. He is a data scientist at Absolutdata.

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England

England

Aaron England earned a Ph.D from the University of Utah in Exercise and Sports Science with a cognate in Biostatistics. Currently, he resides in Scottsdale, Arizona where he works as a data scientist at Natural Partners Fullscript.

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Mohamed Noordeen Alaudeen

Mohamed Noordeen Alaudeen

Mohamed Noordeen Alaudeen is a lead data scientist at Logitech. Noordeen has 7+ years of experience in building and developing end-to-end BigData and Deep Neural Network Systems. It all started when he decided to engage the rest of his life for data science. He is Seasonal data science and big data trainer with both Imarticus Learning and Great Learning, which are two of the renowned data science institutes in India. Apart from his teaching, he does contribute his work to open-source. He has over 90+ repositories on GitHub, which have open-sourced his technical work and data science material. He is an active influencer( with over 22,000+ connections) on Linkedin, helping the data science community.

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