Complete Machine Learning and Deep Learning With H2O in R (Udemy.com)
H2O:Master Powerful R Package For Machine Learning, Artificial Neural Networks (ANN) and Deep Learning
Created by: Minerva Singh
Last updated November 2023
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
How it compares
Against the other 155 rated Udemy courses in Machine Learning on CourseDuck:
- Rating4.5 out of 5higher than 62% of them
- Reviews149#114 of 156 by number of reviews
- Length4.4 hoursshorter than 68% of them
- Last updatedNovember 2023more recently than 54% of them
Course 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.
JOIN MY COURSE NOW!
Instructor Details
- 4.5 Rating
149 Reviews
Minerva Singh
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly
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Reviews
By Raj Kumar Majumdar on 9/9/2021
The course contains vital and valuable information about the utility of various tools with application in R. I will be able to apply the concepts to my work in order to enhance the quality of my work several notches. The knowledge of the instructor about the subject is impressive and her delivery is engaging.
By Rajesh Kumar on 8/2/2021
The course is beyond my expectations. It is a powerful course concerning machine learning, artificial neural networks and deep learning. The valuable contents of the course have significant practical usages. I will greatly benefit out of this course. The instructor's knowledge of the subject is deep and profound and her delivery forceful.
By RKM singh on 6/18/2021
The course has significantly enhanced my knowledge base of the subject. Going through the well-arranged lectures is an amazing experience. The instructor has complete mastery over the subject and command over delivery.
By Anonymized User on 5/20/2021
The course is ideally suited to cater to my requirements. The instructor has designed the lectures in an excellent way. The entire exercise of going through the lectures is quite engrossing. The instructor has full command over the subject.
By Amin Khan on 7/18/2020
My experience with this course has been extremely positive. This course was a large catalyst in a new perspective and outlook that I have developed this semester. I was surprised at how much of the class was relevant to current events and…race relations .
By Shiekh Hasina on 7/16/2020
Great for learning the basics! Love the instructor. She have a great attitude, and her commitment just inspires me to try to give something back to the community as well.
By Cristian Dinu on 6/17/2020
On the market there are only a few courses of H2O. (3) I was hoping since is a "complete" course to have an integral view . Where is tuning ? RTFM . Is really more important "install R and R Studio", read csv Excel and "what is Machine Learning" then tuning ? First 4 sections are out of scope of this couse (18+25+48+11) mins ~ 1.7 hours from 4.5 . I recommend to be revised .
By Diego Carvalho do Nascimento on 5/17/2020
This course could have better video editing in the way that processing time would not necessarily make part of the class. Although I believe this course gives few good tips, codes and datasets are good to start working with H2O package.
By Georgios Konstantopoulos on 4/6/2020
It is a bit slow. But, it is at the very beginning. The latter sections are pretty interesting, deployable in real-life problems, and it is also possible to offer solutions, which are time-efficient. Also, this course can be completed in little time, which is a strength regarding the skills obtained!
By Raymond Johnson on 3/9/2020
So far the course has met my expectation. The material is easy to follow. Some of the content needs to be update (descriptions of data sets, missing files).
Quality Score
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Overall Score : 90 / 100











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