General Boosting & Bagging
Build an ensemble of machine learning algorithms using boosting and bagging methods. This course teaches building and applying prediction functions with a strong focus on the practical application of machine learning using boosting and bagging methods. The course path will include a range of model based and algorithmic machine learning methods such as Random Forest, Boosting & Bagging methods and Support Vector Machines.
Basic understanding of statistics, classification and regression techniques, Language of R, and the basics of machine learning
R
3 months
4 weeks
General Boosting & Bagging
CURRICULUM
The course contains 4 modules.
Most students are able to complete all modules in 4 weeks or less
CASE STUDIES COVERED
Fraud Detection using Adabag
GBM packages of R
WHAT YOU GET
Jigsaw Learning Center
Gain free access to a variety of resources like handouts, reference material, guides, lecture transcripts and student forums for your course duration
Faculty Assistance
Get all your course-related queries solved by the Jigsaw Faculty via email, phone and chat for your program duration.
General Support
We have your back! Our student support teams are available on the weekends for call and 24/7 on email for your course duration
Mobile App
Learn on the go, wherever you are with mobile access to Jigsaw's course videos.
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In case you still have any unanswered questions, we encourage you to register for an upcoming webinar.
