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.
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Practical Machine Learning on H2O
H2O
About this Course
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Intermediate Level
Approx. 31 hours to complete
Suggested: 5 hours/week
English
Subtitles: English
Syllabus - What you will learn from this course
2 hours to complete
H2O AND THE FUNDAMENTALS
2 hours to complete
11 videos (Total 64 min), 4 readings, 5 quizzes
11 videos
Welcome!2m
What's In Week One?2m
Need To Know5m
Preinstall #1 (with Linux)7m
Preinstall #2 (with Windows)4m
Installing H2O4m
A Quick Deep Learning!19m
AutoML6m
Types Of Models7m
Where To Go With Questions2m
Summary1m
4 readings
Further Reading: Course Prerequisites10m
Pre-Install Summary10m
Additional Install Information10m
Further Reading: Getting Help10m
5 practice exercises
Do You Have What It Takes?10m
Quick Preinstall Check6m
Quick Install Check4m
Model types4m
Week One Exam14m
4 hours to complete
Trees And Overfitting
4 hours to complete
15 videos (Total 57 min), 1 reading, 4 quizzes
15 videos
Decision Trees3m
Random Forest2m
Random Forest in H2O (Iris)4m
GBM2m
GBM in H2O (Iris)3m
Importing From Client4m
Artificial Data Sets6m
Overfitting and Train/Valid/Test4m
Train/Valid/Test in H2O3m
GBM in H2O (artificial data)4m
Let's Overfit A GBM!4m
Cross-validation in H2O (GBM)7m
About the peer review task3m
Week Two Summary44s
1 reading
Further Reading: Tree Algorithms10m
3 practice exercises
Decision Trees4m
Tree Algorithms8m
On cross-validation and over-fitting18m
2 hours to complete
LINEAR MODELS AND MORE
2 hours to complete
9 videos (Total 56 min), 4 readings, 3 quizzes
9 videos
Loading From Remote Sources6m
Exporting Data From H2O2m
Exploring With GLMs11m
Naive Bayes4m
Data Manipulation, Statistics11m
Grid Search8m
Applying Grids8m
Summary50s
4 readings
More on loading and saving10m
Further Reading: GLMs, Naive Bayes10m
Further Reading: Data Manipulation10m
Further Reading: Grid Search10m
3 practice exercises
Load/Save4m
GLMs6m
Week Three Exam16m
4 hours to complete
Deep Learning
4 hours to complete
11 videos (Total 66 min), 2 readings, 6 quizzes
11 videos
Load & Save Models1m
Binding data tables6m
Merging and joins3m
Neural Networks6m
Deep Learning Part 19m
Deep Learning Part 210m
Deep Learning with Grids11m
Regression with Deep Learning7m
Introducing The Graded Task3m
Summary Of Week Four1m
2 readings
More Neural Net Theory10m
Extension Project Ideas10m
5 practice exercises
Early Stopping6m
Binding4m
Merging4m
Deep Learning Basics6m
More Deep Learning4m
4.5
12 ReviewsTop reviews from Practical Machine Learning on H2O
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.
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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