Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
Course Outline
Introduction
Setting up H2O
Overview of H2O Features and Architecture
Navigating the H2O WebUI
Preparing the Dataset
Working with Decision Tree Models
Creating a Linear Model
Real-time Data Scoring in H2O
Creating a Random Forest Model
Creating GBMs
Analyzing Hadoop Data
Creating a Deep Learning Model
Creating an Unsupervised Learning Model
Using H2O AutoML to Automate the Model Evaluation Process
Troubleshooting
Summary and Conclusion
Requirements
- Programming experience in Python, R, Scala, or Java.
Audience
- Data scientists
- Data analysts
- Developers
Testimonials
He was very informative and helpful.
Pratheep Ravy
the scope of material
Maciej Jonczyk
systematizing knowledge in the field of ML
Orange Polska
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
The remote classroom setting worked very well
- Trimac Management Services LP
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
The many practical examples / assignments that we went through were great. For me, I learn better by seeing examples and applying them elsewhere. The use of real data and applying what was taught against it was extremely valuable. Michaels PowerPoint presentations and his ability to work through each solution was invaluable.
- Trimac Management Services LP
The exercises.
Elena Velkova - CEED Bulgaria
Practical exercises with R were very helpful.
CEED Bulgaria
Related Courses
Genetic Algorithms
28 hours
Intelligent Testing
14 hours
Matlab for Predictive Analytics
21 hours
Predictive Modelling with R
14 hours
Visual Analytics – Data science
14 hours
DataRobot
7 hours
OptaPlanner in Practice
21 hours