Book Image

Hands-On Data Science with Anaconda [Video]

By : Dr. Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda [Video]

By: Dr. Yuxing Yan, James Yan

Overview of this book

<p>Anaconda is an open-source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting the Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world with ease</p> <p>Throughout this course, you will learn how to use different packages, with Anaconda to get the best results. You will learn how to efficiently use Conda — the package, dependency, and environment manager for Anaconda. You will also be introduced to several powerful features of Anaconda. You will learn how to build scalable and functionally efficient packages, and how to perform heterogeneous data exploration, distributed computing, and more. You will learn to discover and share packages, notebooks, and environments to increase productivity. You will also learn about Anaconda Accelerate, a feature that can help you to achieve SLAs easily and optimize computational power</p> <p>The code bundle for this video course is available at - <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Hands-On-Data-Science-with-Anaconda-Video-" target="blank">https://github.com/PacktPublishing/Hands-On-Data-Science-with-Anaconda-Video-</a></p> <h1>Style and Approach</h1> <p>This course is your step-by-step guide, full of use cases, examples, and illustrations to help you master Anaconda concepts.</p>
Table of Contents (9 chapters)
Chapter 8
Optimization in Anaconda
Content Locked
Section 3
Implementation of Supervised Learning via R
The R package RTextTools is about automatic text classification via supervised learning. It is a machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. - Execute examples using the Rattle package - Print the summary of the logistic regression model - Use the print_algorithms() function to look at the types of existing algorithms