与H2O一起完成机器学习和深度学习

Complete Machine Learning and Deep Learning With H2O in R
H2O:掌握强大的机器学习，人工神经网络和深度学习的R包

你会学到:
能够将R的力量用于实际的数据科学
学习与监督和非监督学习相关的重要概念
用R中强大的H2O包实现真实生活数据的监督和非监督学习
用R中强大的H2O包实现现实生活数据的无监督学习
用R中强大的H2O包在真实数据上实现人工神经网络
用R中强大的H2O包在真实生活数据上实现深度神经网络(DNN)

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2 Ch
语言：英语+中英文字幕（云桥网络 机译） |时长:39节课(4h 21m) |大小解压后:2.88 GB 含课程文件

要求
能够在计算机上操作和安装软件
以前接触过常见的机器学习术语，如无监督和监督学习
之前接触过什么是神经网络&它们可以用来做什么
能够在R中安装软件包

描述
你的H2O完全指南:强大的机器学习和深度学习的R包

本课程涵盖了H2O R数据科学包的主要方面。如果您选修了本课程，您可以不选修其他课程或购买基于R的数据科学书籍，因为您将拥有非常强大的R支持数据科学框架的关键。

在这个大数据时代，全球各地的公司都使用R来筛选他们所掌握的大量信息。通过强大的框架精通机器学习、神经网络和深度学习，H2O在R，你可以给你的公司一个竞争优势，并推动你的职业生涯更上一层楼！


向专家数据科学家学习:

我叫密涅瓦·辛格，毕业于牛津大学地理与环境专业。我在英国剑桥大学获得博士学位，专攻数据科学模型。

我在使用数据科学相关技术分析来自不同来源的真实数据以及为国际同行评审期刊制作出版物方面有5年以上的经验。

在我的研究过程中，我意识到几乎所有的R数据科学课程和书籍都没有说明这个主题的多维性。

本课程将为您提供实用神经网络和深度学习主要方面的坚实基础。

与其他R导师不同，我深入挖掘R的数据科学特性，为您提供独一无二的数据科学基础…

您将一路从执行数据读取和清理到最终实现强大的神经网络和深度学习算法，并使用r评估它们的性能。

除其他外:

您将被介绍到强大的基于R的深度学习包，如H2O。

您将在没有行话的情况下了解机器学习的重要概念。

您将学习如何使用H2O框架实现监督和非监督算法

确定最重要的变量。

用H2O框架实现人工神经网络和深度神经网络

在框架内使用真实数据

不需要事先的研究或统计/机器学习知识:

您将从吸收最有价值的R数据科学基础和技术开始。我使用易于理解的实践方法来简化和解决r中最困难的概念。

我的课程将帮助你使用从不同来源获得的真实数据来实现这些方法。许多课程使用虚构的数据，这并不能让学生在现实生活中实现基于R的数据科学。

学完本课程后，您将很容易地使用数据科学软件包H2O在r .中实现新颖的深度学习技术。您将接触到真实的数据，包括您将学会预处理和建模的真实影像数据

您甚至可以理解底层概念，从而了解什么算法和方法最适合您的数据。

我们还将使用真实数据，您将可以访问课程中使用的所有代码和数据。

这门课是给谁的
想要掌握数据科学R & R工作室环境的人
之前接触过监督学习等常见机器学习概念的人
希望学习在真实数据上实现神经网络的学生
希望学习的学生在R
学生希望在R
学生希望掌握一个强大的数据科学框架，H2O机器学习


MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 39 lectures (4h 21m) | Size: 2.73 GB

H2O:Master Powerful R Package For Machine Learning, Artificial Neural Networks (ANN) and Deep Learning

What you’ll learn:
Be Able To Harness The Power Of R For Practical Data Science
Learn the Important Concepts Associated With Supervised and Unsupervised Learning
Implement Supervised and Unsupervised Learning on Real Life Data With the Powerful H2O Package in R
Implement Unsupervised Learning on Real Life Data With the Powerful H2O Package in R
Implement Artificial Neural Networks (ANN) on Real Life Data With the Powerful H2O Package in R
Implement Deep Neural Networks (DNN) on Real Life Data With the Powerful H2O Package in R

Requirements
Be Able To Operate & Install Software On A Computer
Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
Prior Exposure To What Neural Networks Are & What They Can Be Used For
Be Able to Install Packages in R

Description
YOUR COMPLETE GUIDE TO H2O: POWERFUL R PACKAGE FOR MACHINE LEARNING, & DEEP LEARNING IN R

This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R based data science as you will have the keys to a very powerful R supported data science framework.

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks and deep learning via a powerful framework, H2O in R, you can give your company a competitive edge and boost your career to the next level!

LEARN FROM AN EXPERT DATA SCIENTIST:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I finished a PhD at Cambridge University, UK, where I specialized in data science models.

I have +5 years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.

Over the course of my research, I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic.

This course will give you a robust grounding in the main aspects of practical neural networks and deep learning.

Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science…

You will go all the way from carrying out data reading & cleaning to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.

Among other things:

You will be introduced to powerful R-based deep learning packages such as H2O.

You will be introduced to important concepts of machine learning without the jargon.

You will learn how to implement both supervised and unsupervised algorithms using the H2O framework

Identify the most important variables.

Implement both Artificial Neural Networks (ANN) and Deep Neural Networks (DNNs) with the H2O framework

Work with real data within the framework

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.

After taking this course, you’ll easily use the data science package H2O to implement novel deep learning techniques in R. You will get your hands dirty with real-life data, including real-life imagery data which you will learn to pre-process and model

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will also work with real data and you will have access to all the code and data used in the course.

Who this course is for
People Wanting To Master The R & R Studio Environment For Data Science
Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning
Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R
Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R
Students Wishing To Implement Supervised and Unsupervised Learning on Real Life Data in R
Students Wishing to Master a Powerful Data Science Framework, H2O For Machine Learning in R
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