À propos de ce cours : 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.
Créé par : H2O
Enseigné par : Darren Cook
| Niveau | Intermédiaire |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
H2O AND THE FUNDAMENTALS
11 vidéos, 4 lectures, 3 quiz pour s'exercer
- Vidéo: What's In Week One?
- Vidéo: Need To Know
- Reading: Further Reading: Course Prerequisites
- Practice Quiz: Do You Have What It Takes?
- Vidéo: Preinstall #1 (with Linux)
- Vidéo: Preinstall #2 (with Windows)
- Reading: Pre-Install Summary
- Practice Quiz: Quick Preinstall Check
- Vidéo: Installing H2O
- Reading: Additional Install Information
- Vidéo: A Quick Deep Learning!
- Vidéo: AutoML
- Vidéo: Types Of Models
- Practice Quiz: Model types
- Vidéo: Where To Go With Questions
- Reading: Further Reading: Getting Help
- Vidéo: Summary
Noté: Quick Install Check
Noté: Week One Exam
SEMAINE 2
Trees And Overfitting
15 vidéos, 1 lecture, 2 quiz pour s'exercer
- Vidéo: Decision Trees
- Practice Quiz: Decision Trees
- Vidéo: Random Forest
- Vidéo: Random Forest in H2O (Iris)
- Vidéo: GBM
- Vidéo: GBM in H2O (Iris)
- Vidéo: Importing From Client
- Vidéo: Artificial Data Sets
- Vidéo: Overfitting and Train/Valid/Test
- Vidéo: Train/Valid/Test in H2O
- Vidéo: GBM in H2O (artificial data)
- Vidéo: Let's Overfit A GBM!
- Vidéo: Cross-validation in H2O (GBM)
- Practice Quiz: On cross-validation and over-fitting
- Vidéo: About the peer review task
- Reading: Further Reading: Tree Algorithms
- Vidéo: Week Two Summary
Noté: Tree Algorithms
Noté: Articial Data And Overfitting
SEMAINE 3
LINEAR MODELS AND MORE
9 vidéos, 4 lectures, 2 quiz pour s'exercer
- Vidéo: Loading From Remote Sources
- Vidéo: Exporting Data From H2O
- Reading: More on loading and saving
- Practice Quiz: Load/Save
- Vidéo: Exploring With GLMs
- Vidéo: Naive Bayes
- Reading: Further Reading: GLMs, Naive Bayes
- Practice Quiz: GLMs
- Vidéo: Data Manipulation, Statistics
- Reading: Further Reading: Data Manipulation
- Vidéo: Grid Search
- Vidéo: Applying Grids
- Reading: Further Reading: Grid Search
- Vidéo: Summary
Noté: Week Three Exam
SEMAINE 4
Deep Learning
11 vidéos, 2 lectures, 4 quiz pour s'exercer
- Practice Quiz: Early Stopping
- Vidéo: Load & Save Models
- Vidéo: Binding data tables
- Practice Quiz: Binding
- Vidéo: Merging and joins
- Vidéo: Neural Networks
- Reading: More Neural Net Theory
- Vidéo: Deep Learning Part 1
- Vidéo: Deep Learning Part 2
- Practice Quiz: Deep Learning Basics
- Vidéo: Deep Learning with Grids
- Vidéo: Regression with Deep Learning
- Practice Quiz: More Deep Learning
- Vidéo: Introducing The Graded Task
- Vidéo: Summary Of Week Four
- Reading: Extension Project Ideas
Noté: Merging
Noté: Deep Learning
SEMAINE 5
UNSUPERVISED LEARNING
10 vidéos, 2 lectures, 1 quiz pour s'exercer
- Vidéo: Autoencoders
- Vidéo: Using Autoencoders
- Practice Quiz: Autoencoders
- Vidéo: PCA And GLRM
- Reading: Further Reading: PCA, GLRM
- Vidéo: Clustering, K-Means
- Reading: Further Reading: Clustering
- Vidéo: Data Repair #1
- Vidéo: Data Repair #2
- Vidéo: Hands-on Data Repair
- Vidéo: Next Week's Project
- Vidéo: Week Five Summary
Noté: Unsupervised Learning
Noté: Week Five Exam
SEMAINE 6
Everything Else!
9 vidéos, 2 lectures, 1 quiz pour s'exercer
- Vidéo: Ensembles
- Vidéo: Stacked Ensembles In H2O
- Reading: Further Reading: Ensembles
- Practice Quiz: Ensembles
- Reading: Final Task: advice
- Vidéo: Pojo And Mojo
- Vidéo: Clusters
- Vidéo: Deep Water
- Vidéo: Driverless AI
- Vidéo: H2O4GPU
- Vidéo: Week Six Summary
Noté: Final Project
FAQ
Comment cela fonctionne
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Créateurs
H2O
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
H2O platform provides the least friction to get started with Machine Learning and Data Science for large scale data. It is very easy to setup, load and analyze data, helps in moving fast and get insights quickly. This course assumes some background knowledge in Stats, Linear Algebra, Calculus but the course work itself doesn't let you down. I would like to see more deep dive courses on various powerful algos from H2O. Thanks Coursera and H2O for offering this course.
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RE
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.