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
6 months
12 weeks
General Boosting & Bagging
CURRICULUM
The course contains 4 modules.
Most students are able to complete all modules in 12 weeks or less
CASE STUDIES COVERED
Fraud Detection using Adabag
GBM packages of R
WHAT YOU GET
Video lectures
You get to view 3 hours of video lectures, as many times as you wish to.
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
Support
Our student support teams are available to assist you 24/7 on email
Mobile App
Learn on the go, wherever you are with a LIFETIME* access to Jigsaw's course videos. (*T&C Apply)
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In case you still have any unanswered questions, we encourage you to register for an upcoming webinar.
