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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

Python Libraries

Throughout this book, we'll be using various Python libraries, including pandas, Matplotlib, Seaborn, and scikit-learn.

pandas

pandas is an open source package that has many functions for loading and processing data in order to prepare it for machine learning tasks. It also has tools that can be used to analyze and manipulate data. Data can be read from many formats using pandas. We will mainly be using CSV data throughout this book. To read CSV data, you can use the read_csv() function by passing filename.csv as an argument. An example of this is shown here:

>>> import pandas as pd
>>> pd.read_csv("data.csv")

In the preceding code, pd is an alias name given to pandas. It is not mandatory to give an alias. To visualize a pandas DataFrame, you can use the head() function to list the top five rows. This will be demonstrated in one of the following exercises.

Note

Please visit the following link to learn more about pandas...

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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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