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Practical Machine Learning on H2O
  • Level Professional
  • المدة 24 ساعات hours
  • الطبع بواسطة H2O.ai
  • Offered by

عن

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.

الوحدات

Course Introduction

    • 1 Videos
Show info about module content

1 Videos

  • Welcome!

Prerequisite Knowledge

    • 2 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Do You Have What It Takes?

2 Videos

  • What's In Week One?
  • Need To Know

1 Readings

  • Further Reading: Course Prerequisites

Installation

    • 3 Videos
    • 2 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Quick Preinstall Check
  • Quick Install Check

3 Videos

  • Preinstall #1 (with Linux)
  • Preinstall #2 (with Windows)
  • Installing H2O

2 Readings

  • Pre-Install Summary
  • Additional Install Information

Using H2O

    • 2 Videos
Show info about module content

2 Videos

  • A Quick Deep Learning!
  • AutoML

Model Types

    • 1 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Model types

1 Videos

  • Types Of Models

Help!

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week One Exam

2 Videos

  • Where To Go With Questions
  • Summary

1 Readings

  • Further Reading: Getting Help

Tree Algorithms

    • 6 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Decision Trees
  • Tree Algorithms

6 Videos

  • Weekly Intro
  • Decision Trees
  • Random Forest
  • Random Forest in H2O (Iris)
  • GBM
  • GBM in H2O (Iris)

Importing, Generating And Over-Fitting

    • 7 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • On cross-validation and over-fitting

7 Videos

  • 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)

Summary (and assignment!)

    • 2 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Articial Data And Overfitting

2 Videos

  • About the peer review task
  • Week Two Summary

1 Readings

  • Further Reading: Tree Algorithms

Loading And Saving

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Load/Save

3 Videos

  • Exploring The Universe
  • Loading From Remote Sources
  • Exporting Data From H2O

1 Readings

  • More on loading and saving

Linear Models and Naive Bayes

    • 2 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • GLMs

2 Videos

  • Exploring With GLMs
  • Naive Bayes

1 Readings

  • Further Reading: GLMs, Naive Bayes

Data Manipulation

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Data Manipulation, Statistics

1 Readings

  • Further Reading: Data Manipulation

Grids

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Week Three Exam

3 Videos

  • Grid Search
  • Applying Grids
  • Summary

1 Readings

  • Further Reading: Grid Search

Early Stopping

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Early Stopping

2 Videos

  • Weekly Introduction and Early Stopping
  • Load & Save Models

Binding and Merging Data

    • 2 Videos
    • 2 Assignment
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2 Assignment

  • Binding
  • Merging

2 Videos

  • Binding data tables
  • Merging and joins

Deep Learning

    • 5 Videos
    • 1 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Deep Learning Basics
  • More Deep Learning

5 Videos

  • Neural Networks
  • Deep Learning Part 1
  • Deep Learning Part 2
  • Deep Learning with Grids
  • Regression with Deep Learning

1 Readings

  • More Neural Net Theory

Summary and Assignment

    • 2 Videos
    • 1 Readings
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Deep Learning

2 Videos

  • Introducing The Graded Task
  • Summary Of Week Four

1 Readings

  • Extension Project Ideas

Autoencoders

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Autoencoders

3 Videos

  • Week Five Is Unsupervised
  • Autoencoders
  • Using Autoencoders

More Dimension Reduction

    • 1 Videos
    • 1 Readings
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1 Videos

  • PCA And GLRM

1 Readings

  • Further Reading: PCA, GLRM

Clustering

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Unsupervised Learning

1 Videos

  • Clustering, K-Means

1 Readings

  • Further Reading: Clustering

Handling Missing Data

    • 3 Videos
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3 Videos

  • Data Repair #1
  • Data Repair #2
  • Hands-on Data Repair

EOW (End Of Week)

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Week Five Exam

2 Videos

  • Next Week's Project
  • Week Five Summary

Ensembles

    • 3 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Ensembles

3 Videos

  • Pulling It All Together
  • Ensembles
  • Stacked Ensembles In H2O

2 Readings

  • Further Reading: Ensembles
  • Final Task: advice

Other H2O Technologies

    • 5 Videos
Show info about module content

5 Videos

  • Pojo And Mojo
  • Clusters
  • Deep Water
  • Driverless AI
  • H2O4GPU

Final Project

    • 1 Videos
    • 1 PeerReview
Show info about module content

1 Peer Review

  • Final Project

1 Videos

  • Week Six Summary

Auto Summary

Discover the essentials of Practical Machine Learning on H2O with this comprehensive course in Data Science & AI. Taught by Coursera, it covers core techniques, including linear models, random forests, GBMs, and deep learning, suitable even for beginners. With a duration of 1440 minutes, the course offers flexible subscription options: Starter, Professional, and Paid. Ideal for professionals aiming to enhance their machine learning skills and model evaluation capabilities.

Darren Cook
Instructor

Darren Cook