Machine Learning with MATLAB

Learn to create regression, classification, and clustering models and improve performance for data analytics

TechSource Systems Pte Ltd

Course
Highlights

This two-day course focuses on data analytics and machine learning techniques in MATLAB® using functionality within Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. Topics include:

  • Organizing and preprocessing data
  • Clustering data
  • Creating classification and regression models
  • Interpreting and evaluating models
  • Simplifying data sets
  • Using ensembles to improve model performance
TechSource Systems Pte Ltd

Who Should
Attend

This course is intended for engineer, researchers, data scientists, and managers, who are involved in the design of intelligent systems that can automatically produce models which can analyse bigger, more complex data and deliver faster and more accurate results.

TechSource Systems Pte Ltd

Course
Prerequisites

MATLAB Fundamentals

TechSource Systems Pte Ltd

Course
Benefits

Upon the completion of the course, the participants will be able to:

  • use unsupervised learning techniques to discover natural pattern
  • use supervised learning techniques for regression and classification
  • reduce feature dimension
  • improve the regression and classification model

Partners

TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

TechSource Systems is MathWorks Authorised Reseller and Training Partner

Upcoming Program

  • Please keep me posted on the next schedule
  • Please contact me to arrange customized/ in-house training

Course Outline

Importing and Organizing Data

Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.

  • Data types
  • Tables
  • Categorical data
  • Data preparation
TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

Finding Natural Patterns in Data

Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

  • Unsupervised learning
  • Clustering methods
  • Cluster evaluation and interpretation

Building Classification Models

Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.

  • Supervised learning
  • Training and validation
  • Classification methods
TechSource Systems Pte Ltd

Improving Predictive Models

Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.

  • Cross validation
  • Hyperparameter optimization
  • Feature transformation
  • Feature selection
  • Ensemble learning
TechSource Systems Pte Ltd
TechSource Systems Pte Ltd

Building Regression Models

Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.

  • Parametric regression methods
  • Nonparametric regression methods
  • Evaluation of regression models

Creating Neural Networks

Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.

  • Clustering with Self-Organizing Maps
  • Classification with feed-forward networks
  • Regression with feed-forward networks
TechSource Systems Pte Ltd
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