Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Chapter 6. Supervised Machine Learning

It is often believed that data science is machine learning, which means in data science, we only train models of machine learning. But data science is much more than that. Data science involves understanding data, gathering data, munging data, taking the meaning out of that data, and then machine learning if needed.

In my opinion, machine learning is the most exciting field that exists today. With huge amounts of data that is readily available, we can gather invaluable knowledge. Lots of companies have made their machine learning libraries accessible and there are lots of open source alternatives that exist.

In this chapter, you will study the following topics:

  • What is machine learning?

  • Types of machine learning

  • What is overfitting and underfitting?

  • Bias-variance trade-off

  • Feature extraction and selection

  • Decision trees

  • Naïve Bayes classifier