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Practical Machine Learning on H2O

Approx. 24 hours to complete

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Course Summary

Learn to build powerful machine learning models using H2O software in this comprehensive course. Discover how to use H2O machine learning algorithms to solve real-world problems.

Key Learning Points

Learn to build and implement machine learning models using H2O
Understand how to leverage H2O’s distributed computing capabilities to handle large datasets
Gain hands-on experience in building predictive models using H2O

Job Positions & Salaries of people who have taken this course might have

Data Scientist
- USA: $113,309
- India: ₹1,103,388
- Spain: €35,000
Machine Learning Engineer
- USA: $139,254
- India: ₹1,493,214
- Spain: €45,000
Big Data Developer
- USA: $115,912
- India: ₹1,056,222
- Spain: €38,000

Learning Outcomes

Build and implement machine learning models using H2O
Leverage H2O’s distributed computing capabilities to handle large datasets
Gain hands-on experience in building predictive models using H2O

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning concepts
Experience with programming in Python

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Machine Learning
Data Science Methodology

Related Education Paths

Notable People in This Field

Erin LeDell
Navdeep Gill

Related Books

Description

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.

Outline

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

Summary of User Reviews

Discover the power of machine learning with H2O! This course has received high praise from students for its engaging content and knowledgeable instructors. Many users have found the hands-on approach to be extremely helpful in understanding the concepts. Overall, this course is highly recommended for anyone interested in learning about machine learning with H2O.

Key Aspect Users Liked About This Course

The hands-on approach to learning machine learning concepts

Pros from User Reviews

Engaging and knowledgeable instructors
Practical examples and hands-on exercises
In-depth coverage of machine learning concepts

Cons from User Reviews

Some users found the pace of the course to be too slow
The course may be challenging for beginners with no prior programming experience
The course focuses primarily on using H2O for machine learning, so it may not be suitable for users interested in learning other tools or techniques

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