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
Python, 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
Faculty & Technical Support
Ticketing system for faculty and technical support
Mobile App Access
Learn on the go, wherever you are with mobile access to Jigsaw's course videos.
Case Studies
Multiple, industry relevant data sets and case studies across different industries
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This course is most effective as part of our Full Stack program. Talk to our student counselors for more details.
