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
- 阅读: Further Reading: Course Prerequisites
- 练习测验: Do You Have What It Takes?
- 视频: Preinstall #1 (with Linux)
- 视频: Preinstall #2 (with Windows)
- 阅读: Pre-Install Summary
- 练习测验: Quick Preinstall Check
- 视频: Installing H2O
- 阅读: Additional Install Information
- 视频: A Quick Deep Learning!
- 视频: AutoML
- 视频: Types Of Models
- 练习测验: Model types
- 视频: Where To Go With Questions
- 阅读: 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
- 练习测验: 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)
- 练习测验: On cross-validation and over-fitting
- 视频: About the peer review task
- 阅读: 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
- 阅读: More on loading and saving
- 练习测验: Load/Save
- 视频: Exploring With GLMs
- 视频: Naive Bayes
- 阅读: Further Reading: GLMs, Naive Bayes
- 练习测验: GLMs
- 视频: Data Manipulation, Statistics
- 阅读: Further Reading: Data Manipulation
- 视频: Grid Search
- 视频: Applying Grids
- 阅读: 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
- 练习测验: Early Stopping
- 视频: Load & Save Models
- 视频: Binding data tables
- 练习测验: Binding
- 视频: Merging and joins
- 视频: Neural Networks
- 阅读: More Neural Net Theory
- 视频: Deep Learning Part 1
- 视频: Deep Learning Part 2
- 练习测验: Deep Learning Basics
- 视频: Deep Learning with Grids
- 视频: Regression with Deep Learning
- 练习测验: More Deep Learning
- 视频: Introducing The Graded Task
- 视频: Summary Of Week Four
- 阅读: 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
- 练习测验: Autoencoders
- 视频: PCA And GLRM
- 阅读: Further Reading: PCA, GLRM
- 视频: Clustering, K-Means
- 阅读: 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
- 阅读: Further Reading: Ensembles
- 练习测验: Ensembles
- 阅读: 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
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
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
VT
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.