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 2
Classification
Supervised learning problems can be further divided into two groups: classification and regression. For the classification problem, the output variable, such as y, could be a binary variable (that is, 0 or 1) or several categories. For regression, variables or values could be discrete or continuous. In this video, we will discuss the concept of distance between group members within the same group and between groups. - Use the Iris dataset to separate plants into k-groups - Apply the naive Bayes methodology to predict the survival rate of the Titanic dataset