In this course, we will learn all the core techniques needed to make effective use of H2O. Even if you have no prior experience of machine learning, even if your math is weak, by the end of this course you will be able to make machine learning models using a variety of algorithms. We will be using linear models, random forest, GBMs and of course deep learning, as well as some unsupervised learning algorithms. You will also be able to evaluate your models and choose the best model to suit not just your data but the other business restraints you may be under.
What's included
11 videos4 readings5 assignments
Show info about module content
11 videos•Total 64 minutes
Welcome!•2 minutes
What's In Week One?•3 minutes
Need To Know•5 minutes
Preinstall #1 (with Linux)•7 minutes
Preinstall #2 (with Windows)•4 minutes
Installing H2O•5 minutes
A Quick Deep Learning!•20 minutes
AutoML•6 minutes
Types Of Models•7 minutes
Where To Go With Questions•3 minutes
Summary•1 minute
4 readings•Total 40 minutes
Further Reading: Course Prerequisites•10 minutes
Pre-Install Summary•10 minutes
Additional Install Information•10 minutes
Further Reading: Getting Help•10 minutes
5 assignments•Total 150 minutes
Quick Install Check•30 minutes
Week One Exam•30 minutes
Do You Have What It Takes?•30 minutes
Quick Preinstall Check•30 minutes
Model types•30 minutes
Trees And Overfitting
Module 2•5 hours to complete
Module details
What's included
15 videos1 reading3 assignments1 peer review
Show info about module content
15 videos•Total 57 minutes
Weekly Intro•2 minutes
Decision Trees•3 minutes
Random Forest•3 minutes
Random Forest in H2O (Iris)•4 minutes
GBM•2 minutes
GBM in H2O (Iris)•4 minutes
Importing From Client•5 minutes
Artificial Data Sets•7 minutes
Overfitting and Train/Valid/Test•5 minutes
Train/Valid/Test in H2O•3 minutes
GBM in H2O (artificial data)•4 minutes
Let's Overfit A GBM!•4 minutes
Cross-validation in H2O (GBM)•7 minutes
About the peer review task•3 minutes
Week Two Summary•1 minute
1 reading•Total 10 minutes
Further Reading: Tree Algorithms•10 minutes
3 assignments•Total 90 minutes
Tree Algorithms•30 minutes
Decision Trees•30 minutes
On cross-validation and over-fitting•30 minutes
1 peer review•Total 120 minutes
Articial Data And Overfitting•120 minutes
LINEAR MODELS AND MORE
Module 3•3 hours to complete
Module details
What's included
9 videos4 readings3 assignments
Show info about module content
9 videos•Total 56 minutes
Exploring The Universe•2 minutes
Loading From Remote Sources•7 minutes
Exporting Data From H2O•2 minutes
Exploring With GLMs•12 minutes
Naive Bayes•4 minutes
Data Manipulation, Statistics•12 minutes
Grid Search•8 minutes
Applying Grids•9 minutes
Summary•1 minute
4 readings•Total 40 minutes
More on loading and saving•10 minutes
Further Reading: GLMs, Naive Bayes•10 minutes
Further Reading: Data Manipulation•10 minutes
Further Reading: Grid Search•10 minutes
3 assignments•Total 90 minutes
Week Three Exam•30 minutes
Load/Save•30 minutes
GLMs•30 minutes
Deep Learning
Module 4•5 hours to complete
Module details
What's included
11 videos2 readings5 assignments1 peer review
Show info about module content
11 videos•Total 66 minutes
Weekly Introduction and Early Stopping•5 minutes
Load & Save Models•1 minute
Binding data tables•6 minutes
Merging and joins•4 minutes
Neural Networks•7 minutes
Deep Learning Part 1•10 minutes
Deep Learning Part 2•10 minutes
Deep Learning with Grids•11 minutes
Regression with Deep Learning•8 minutes
Introducing The Graded Task•3 minutes
Summary Of Week Four•1 minute
2 readings•Total 20 minutes
More Neural Net Theory•10 minutes
Extension Project Ideas•10 minutes
5 assignments•Total 150 minutes
Merging•30 minutes
Early Stopping•30 minutes
Binding•30 minutes
Deep Learning Basics•30 minutes
More Deep Learning•30 minutes
1 peer review•Total 60 minutes
Deep Learning•60 minutes
UNSUPERVISED LEARNING
Module 5•3 hours to complete
Module details
What's included
10 videos2 readings3 assignments
Show info about module content
10 videos•Total 60 minutes
Week Five Is Unsupervised•2 minutes
Autoencoders•5 minutes
Using Autoencoders•16 minutes
PCA And GLRM•6 minutes
Clustering, K-Means•4 minutes
Data Repair #1•2 minutes
Data Repair #2•5 minutes
Hands-on Data Repair•17 minutes
Next Week's Project•2 minutes
Week Five Summary•1 minute
2 readings•Total 21 minutes
Further Reading: PCA, GLRM•10 minutes
Further Reading: Clustering•11 minutes
3 assignments•Total 90 minutes
Unsupervised Learning•30 minutes
Week Five Exam•30 minutes
Autoencoders•30 minutes
Everything Else!
Module 6•5 hours to complete
Module details
What's included
9 videos2 readings1 assignment1 peer review
Show info about module content
9 videos•Total 32 minutes
Pulling It All Together•1 minute
Ensembles•3 minutes
Stacked Ensembles In H2O•11 minutes
Pojo And Mojo•6 minutes
Clusters•3 minutes
Deep Water•3 minutes
Driverless AI•2 minutes
H2O4GPU•2 minutes
Week Six Summary•1 minute
2 readings•Total 20 minutes
Further Reading: Ensembles•10 minutes
Final Task: advice•10 minutes
1 assignment•Total 30 minutes
Ensembles•30 minutes
1 peer review•Total 240 minutes
Final Project•240 minutes
Instructor
Instructor ratings
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
At H2O.ai University, our mission is to deliver exceptional courses on state-of-the-art AI tools. These courses aim to support the H2O ai community in effectively using our tools and achieving certification success. We are committed to fostering a deep understanding and proficiency in AI, empowering our users to excel in their endeavors.
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Learner since 2020
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Learner since 2021
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Learner reviews
4.5
74 reviews
5 stars
67.56%
4 stars
24.32%
3 stars
4.05%
2 stars
1.35%
1 star
2.70%
Showing 3 of 74
R
RE
5·
Reviewed on Sep 10, 2018
I've taken a lot of Coursera classes and this is one of the better classes. It is a good hands-on course and will help students learn more about not only H2O, but also machine learning.
R
RM
4·
Reviewed on Sep 30, 2019
awsome but needs more to explain on autoencoder ,anomely
E
EA
5·
Reviewed on Feb 3, 2019
Great content, a lot of hands-on activities and the instructor is quite good too. By the end of the course, I feel that I have the necessary skills to work with h2o.
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When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.