Complete Machine Learning and Deep Learning With H2O in R

Complete Machine Learning and Deep Learning With H2O in R, available at $84.99, has an average rating of 4.3, with 40 lectures, based on 115 reviews, and has 1065 subscribers.

You will learn about 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 This course is ideal for individuals who are People Wanting To Master The R & R Studio Environment For Data Science or Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning or Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R or Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R or Students Wishing To Implement Supervised and Unsupervised Learning on Real Life Data in R or Students Wishing to Master a Powerful Data Science Framework, H2O For Machine Learning in R It is particularly useful for People Wanting To Master The R & R Studio Environment For Data Science or Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning or Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R or Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R or Students Wishing To Implement Supervised and Unsupervised Learning on Real Life Data in R or Students Wishing to Master a Powerful Data Science Framework, H2O For Machine Learning in R.

Enroll now: Complete Machine Learning and Deep Learning With H2O in R

Summary

Title: Complete Machine Learning and Deep Learning With H2O in R

Price: $84.99

Average Rating: 4.3

Number of Lectures: 40

Number of Published Lectures: 40

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

JOIN MY COURSE NOW!

Course Curriculum

Chapter 1: Welcome To The Course

Lecture 1: Brief Introduction

Lecture 2: Data and Code

Lecture 3: Install R and RStudio

Lecture 4: Common data types

Lecture 5: Install H2o

Chapter 2: Read in Data From Different Sources

Lecture 1: Read CSV and Excel Data

Lecture 2: Read in Data from Online HTML Tables-Part 1

Lecture 3: Read in Data from Online HTML Tables-Part 2

Lecture 4: Read External Data into H2o

Chapter 3: Data Preprocessing (Briefly)

Lecture 1: Basic Data Cleaning in R: Remove NA

Lecture 2: Pre-processing Tasks and the Pipe Operator

Lecture 3: Introduction to Pipe Operators

Lecture 4: The Tidyverse Package

Lecture 5: Exploratory Data Analysis(EDA): Basic Visualizations with R

Chapter 4: Some Theoretical Foundations

Lecture 1: What is Machine Learning?

Lecture 2: Difference Between Supervised & Unsupervised Learning

Chapter 5: Unsupervised Classification with H2o

Lecture 1: Theory of k-Means Clustering

Lecture 2: Implement k-Means Classification

Lecture 3: Principal Component Analysis (PCA): Theory

Lecture 4: Implement PCA With H2O

Chapter 6: Supervised Classification with H2O

Lecture 1: Generalized Linear Models (GLMs): Theory

Lecture 2: GLMs For Binary Classification

Lecture 3: Common Algorithms For Supervised Classification

Lecture 4: Implement Random Forest For Binary Classification Problem

Lecture 5: Measures of Accuracy:Binary Classification

Lecture 6: Implement Random Forest For Multiple Classification Problem

Lecture 7: Gradient Boosting Machines (GBM) for Binary Classification

Chapter 7: Artificial Neural Networks (ANN) and Deep Neural Networks With H2O

Lecture 1: A Brief Introduction to Artificial Intelligence

Lecture 2: Theory Behind ANN and DNN

Lecture 3: Implement an ANN with H2o For Multi-Class Supervised Classification

Lecture 4: What Are Activation Functions? Theory

Lecture 5: Implement a DNN with H2o For Multi-Class Supervised Classification

Lecture 6: Implement a (Less Intensive) DNN with H2o For Supervised Classification

Lecture 7: Identify the Important Predictors

Lecture 8: DNN For Regression

Chapter 8: Deep Learning Based Unsupervised Classification

Lecture 1: Autoencoders for Unsupervised Learning

Lecture 2: Unsupervised Classification with H2o

Lecture 3: More Autoencoders : Credit Card Fraud Detection

Lecture 4: Use the Autoencoder Model for Anomaly Detection

Lecture 5: Posit On POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 3 votes
2 stars: 3 votes
3 stars: 16 votes
4 stars: 18 votes
5 stars: 75 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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