Overview
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Course Outline
- Naive Bayes
- Multinomial models
- Bayesian categorical data analysis
- Discriminant analysis
- Linear regression
- Logistic regression
- GLM
- EM Algorithm
- Mixed Models
- Additive Models
- Classification
- KNN
- Bayesian Graphical Models
- Factor Analysis (FA)
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Support Vector Machines (SVM) for regression and classification
- Boosting
- Ensemble models
- Neural networks
- Hidden Markov Models (HMM)
- Space State Models
- Clustering