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

Machine learning – the process


Machine learning algorithms are trained in keeping with the idea of how the human brain works. They are somewhat similar. Let's discuss the whole process.

The machine learning process can be described in three steps:

  1. Input

  2. Abstraction

  3. Generalization

These three steps are the core of how the machine learning algorithm works. Although the algorithm may or may not be divided or represented in such a way, this explains the overall approach:

  1. The first step concentrates on what data should be there and what shouldn't. On the basis of that, it gathers, stores, and cleans the data as per the requirements.

  2. The second step entails the data being translated to represent the bigger class of data. This is required as we cannot capture everything and our algorithm should not be applicable for only the data that we have.

  3. The third step focuses on the creation of the model or an action that will use this abstracted data, which will be applicable for the broader mass.

So, what should...