Course 805:
Introduction to Machine Learning

(4 days)

 

Course Description

With massive amounts of data available and inexpensive computing power to quickly process the data, it is now possible to find computational solutions to problems previously too expensive and time consuming to solve. The course provides an understanding of how machine learning works. Supervised models and their hyper-parameters are explored to include regression, ridge, lasso, SVM. Clustering of data and anomaly detection are explored. Decision Trees and an introduction to Bayesian Models are included. Exercises are included to allow students to explore how the models works.

Learning Objectives

  • Understand how supervised learning works
  • Know how to use linear regression models: regression, ridge, and lasso
  • Know how to use SVM for non-linear problems
  • Understand anomalies
  • Understand dimension reduction
  • Be introduced to Bayesian models
  • Know how to use classification models
  • Understand clustering
  • Be introduced to decision trees, random forests, and ensembles

Prerequisites

A curiosity about Machine Learning and a desire to learn how to apply it to business and organizational problems.

Who Should Attend

Anyone wanting to understand supervised and unsupervised learning without spending weeks working through the math.

Course Outline

Basic Supervised Learning Model

  • The Parts of an ML Model
  • Training the ML Model

Supervised Regression Models

  • Goodness of Fit Parameters
  • Outliers
  • Learning Rate
  • Multiple Features
  • Normalization and Standardization
  • Polynomial Regression
  • Overfitting
  • Regularization
  • Cross-Validation
  • Ridge

Supervised Classification Models

  • The Classification Model
  • Goodness of Fit
  • Binary Classification
  • Multiclass Classification
  • Solvers and Activation Function
  • Normalization and Standardization
  • Regularization

SVM Models

  • Linear Separation
  • Basic SVM
  • Kernel Modules

Bayesian Models

  • Bayes Theorem
  • Naïve Bayes

Decision Trees

  • Demonstration of a Decision Tree
  • Overview of Decision Tree Model
  • Combining Techniques: Ensembles and Forests

Unsupervised Learning: Clustering

  • What Is Different About Clustering?
  • KNN Clustering
  • K-Mean Clustering
  • Hierarchical Clustering

Unsupervised Learning: Anomaly Detection

  • EllipticEnvelope
  • Anomaly Detection Plotting
  • SVM Anomaly Detection

Dimension Reduction

  • RFE
  • PCA
  • Lasso

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