Duration in Days
Training Scope, Objectives, Outlines, and Expected Outcomes
- Learning Machine Learning Models.
- Understand machine learning algorithms.
- How to assess the model results.
- How to test the model results.
- How to develop machine learning using R,Python and SPSS.
Suggested Course Outlines:
- Labs and hands-on for all the component.
- Certification for the component.
- Naive Bayes
- Multinomial models
- Bayesian categorical data analysis
- Discriminant analysis
- Linear regression
- Logistic regression
- Decision Trees
- Random forests
- EM Algorithm
- Mixed Models
- Additive Models
- Bayesian Graphical Models
- Factor Analysis (FA)
- Principal Component Analysis (PCA)
- Independent Component Analysis (ICA)
- Support Vector Machines (SVM) for regression and classification
- Ensemble models
- Neural networks
- Hidden Markov Models (HMM)
- Space State Models
How the training can possibly add value
- Learning from data.
- Appling machine learning models on data.
- Generate better data analytics reports and predictive models.
- Have the ability to assess the model and choose the algorithms.
- How to test the model and the data using different testing algorithms.