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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

England

Mohamed Noordeen Alaudeen

View More author details

Table of Contents (18) Chapters

Preface

About the Book

1. Chapter 1

2. Introduction to Data Science and Data Pre-Processing FREE CHAPTER

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 Representation

The main objective of machine learning is to build models that understand data and find underlying patterns. In order to do so, it is very important to feed the data in a way that is interpretable by the computer. To feed the data into a model, it must be represented as a table or a matrix of the required dimensions. Converting your data into the correct tabular form is one of the first steps before pre-processing can properly begin.

Data Represented in a Table

Data should be arranged in a two-dimensional space made up of rows and columns. This type of data structure makes it easy to understand the data and pinpoint any problems. An example of some raw data stored as a CSV (comma separated values) file is shown here:

Figure 1.1: Raw data in CSV format

Figure 1.1: Raw data in CSV format

The representation of the same data in a table is as follows:

Figure 1.2: CSV data in table format

Figure 1.2: CSV data in table format

If you compare the data in CSV and table formats...

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Data Science with Python

Published in: Jul 2019

Publisher: Packt

ISBN-13: 9781838552862

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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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