Careers

Machine Learning



Duration in Days
5 Days

Training Scope, Objectives, Outlines, and Expected Outcomes

Objectives: Objectives:
  • 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.
Requirements:
  • Labs and hands-on for all the component.
  • Certification for the component.
Suggested Course Outlines:
  • Naive Bayes
  • Multinomial models
  • Bayesian categorical data analysis
  • Discriminant analysis
  • Linear regression
  • Logistic regression
  • Decision Trees
  • Random forests
  • 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
  • Optimization
Tools
  • Python
  • R
  • SPSS advanced
Expected Accomplishments:
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.
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