关于此课程： 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.
授课大纲
第 1 周
H2O AND THE FUNDAMENTALS
11 个视频, 4 个阅读材料, 3 个练习测验
- 视频: What's In Week One?
- 视频: Need To Know
- Reading: Further Reading: Course Prerequisites
- Practice Quiz: Do You Have What It Takes?
- 视频: Preinstall #1 (with Linux)
- 视频: Preinstall #2 (with Windows)
- Reading: Pre-Install Summary
- Practice Quiz: Quick Preinstall Check
- 视频: Installing H2O
- Reading: Additional Install Information
- 视频: A Quick Deep Learning!
- 视频: AutoML
- 视频: Types Of Models
- Practice Quiz: Model types
- 视频: Where To Go With Questions
- Reading: Further Reading: Getting Help
- 视频: Summary
已评分: Quick Install Check
已评分: Week One Exam
第 2 周
Trees And Overfitting
15 个视频, 1 个阅读材料, 2 个练习测验
- 视频: Decision Trees
- Practice Quiz: Decision Trees
- 视频: Random Forest
- 视频: Random Forest in H2O (Iris)
- 视频: GBM
- 视频: GBM in H2O (Iris)
- 视频: Importing From Client
- 视频: Artificial Data Sets
- 视频: Overfitting and Train/Valid/Test
- 视频: Train/Valid/Test in H2O
- 视频: GBM in H2O (artificial data)
- 视频: Let's Overfit A GBM!
- 视频: Cross-validation in H2O (GBM)
- Practice Quiz: On cross-validation and over-fitting
- 视频: About the peer review task
- Reading: Further Reading: Tree Algorithms
- 视频: Week Two Summary
已评分: Tree Algorithms
已评分: Articial Data And Overfitting
第 3 周
LINEAR MODELS AND MORE
9 个视频, 4 个阅读材料, 2 个练习测验
- 视频: Loading From Remote Sources
- 视频: Exporting Data From H2O
- Reading: More on loading and saving
- Practice Quiz: Load/Save
- 视频: Exploring With GLMs
- 视频: Naive Bayes
- Reading: Further Reading: GLMs, Naive Bayes
- Practice Quiz: GLMs
- 视频: Data Manipulation, Statistics
- Reading: Further Reading: Data Manipulation
- 视频: Grid Search
- 视频: Applying Grids
- Reading: Further Reading: Grid Search
- 视频: Summary
已评分: Week Three Exam
第 4 周
Deep Learning
11 个视频, 2 个阅读材料, 4 个练习测验
- Practice Quiz: Early Stopping
- 视频: Load & Save Models
- 视频: Binding data tables
- Practice Quiz: Binding
- 视频: Merging and joins
- 视频: Neural Networks
- Reading: More Neural Net Theory
- 视频: Deep Learning Part 1
- 视频: Deep Learning Part 2
- Practice Quiz: Deep Learning Basics
- 视频: Deep Learning with Grids
- 视频: Regression with Deep Learning
- Practice Quiz: More Deep Learning
- 视频: Introducing The Graded Task
- 视频: Summary Of Week Four
- Reading: Extension Project Ideas
已评分: Merging
已评分: Deep Learning
第 5 周
UNSUPERVISED LEARNING
10 个视频, 2 个阅读材料, 1 个练习测验
- 视频: Autoencoders
- 视频: Using Autoencoders
- Practice Quiz: Autoencoders
- 视频: PCA And GLRM
- Reading: Further Reading: PCA, GLRM
- 视频: Clustering, K-Means
- Reading: Further Reading: Clustering
- 视频: Data Repair #1
- 视频: Data Repair #2
- 视频: Hands-on Data Repair
- 视频: Next Week's Project
- 视频: Week Five Summary
已评分: Unsupervised Learning
已评分: Week Five Exam
第 6 周
Everything Else!
9 个视频, 2 个阅读材料, 1 个练习测验
- 视频: Ensembles
- 视频: Stacked Ensembles In H2O
- Reading: Further Reading: Ensembles
- Practice Quiz: Ensembles
- Reading: Final Task: advice
- 视频: Pojo And Mojo
- 视频: Clusters
- 视频: Deep Water
- 视频: Driverless AI
- 视频: H2O4GPU
- 视频: Week Six Summary
已评分: Final Project
常见问题解答
运作方式
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.
制作方
H2O
价格
| 购买课程 | |
|---|---|
| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
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.
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.