Practical Machine Learning on H2O
- Offered byCoursera
Practical Machine Learning on H2O at Coursera Overview
Duration | 24 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Practical Machine Learning on H2O at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 24 hours to complete
- English Subtitles: English
Practical Machine Learning on H2O at Coursera Course details
- 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.
Practical Machine Learning on H2O at Coursera Curriculum
H2O AND THE FUNDAMENTALS
Welcome!
What's In Week One?
Need To Know
Preinstall #1 (with Linux)
Preinstall #2 (with Windows)
Installing H2O
A Quick Deep Learning!
AutoML
Types Of Models
Where To Go With Questions
Summary
Further Reading: Course Prerequisites
Pre-Install Summary
Additional Install Information
Further Reading: Getting Help
Do You Have What It Takes?
Quick Preinstall Check
Quick Install Check
Model types
Week One Exam
Trees And Overfitting
Weekly Intro
Decision Trees
Random Forest
Random Forest in H2O (Iris)
GBM
GBM in H2O (Iris)
Importing From Client
Artificial Data Sets
Overfitting and Train/Valid/Test
Train/Valid/Test in H2O
GBM in H2O (artificial data)
Let's Overfit A GBM!
Cross-validation in H2O (GBM)
About the peer review task
Week Two Summary
Further Reading: Tree Algorithms
Decision Trees
Tree Algorithms
On cross-validation and over-fitting
LINEAR MODELS AND MORE
Exploring The Universe
Loading From Remote Sources
Exporting Data From H2O
Exploring With GLMs
Naive Bayes
Data Manipulation, Statistics
Grid Search
Applying Grids
Summary
More on loading and saving
Further Reading: GLMs, Naive Bayes
Further Reading: Data Manipulation
Further Reading: Grid Search
Load/Save
GLMs
Week Three Exam
Deep Learning
Weekly Introduction and Early Stopping
Load & Save Models
Binding data tables
Merging and joins
Neural Networks
Deep Learning Part 1
Deep Learning Part 2
Deep Learning with Grids
Regression with Deep Learning
Introducing The Graded Task
Summary Of Week Four
More Neural Net Theory
Extension Project Ideas
Early Stopping
Binding
Merging
Deep Learning Basics
More Deep Learning
UNSUPERVISED LEARNING
Week Five Is Unsupervised
Autoencoders
Using Autoencoders
PCA And GLRM
Clustering, K-Means
Data Repair #1
Data Repair #2
Hands-on Data Repair
Next Week's Project
Week Five Summary
Further Reading: PCA, GLRM
Further Reading: Clustering
Autoencoders
Unsupervised Learning
Week Five Exam
Everything Else!
Pulling It All Together
Ensembles
Stacked Ensembles In H2O
Pojo And Mojo
Clusters
Deep Water
Driverless AI
H2O4GPU
Week Six Summary
Further Reading: Ensembles
Final Task: advice
Ensembles
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