Course Outline

Introduction to Data mining and Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises

Classification

  • Bayesian refresher
  • Naive Bayes
  • Dicriminant analysis
  • Logistic regression
  • K-Nearest neighbors
  • Support Vector Machines
  • Neural networks
  • Decision trees
  • Exercises

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

Advanced topics

  • Ensemble models
  • Mixed models
  • Boosting
  • Examples

Multidimensional reduction

  • Factor Analysis
  • Principal Component Analysis
  • Examples

Requirements

This course is part of the Data Scientist skill set (Domain: Analytical Techniques and Methods)
  14 Hours
 

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Testimonials

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama

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