MATLAB & Simulink
DATA ANALYSIS AND MACHINE LEARNING
Machine Learning with MATLAB
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 Neural Network 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 highlights techniques for visualization and evaluation of results. Topics include:
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Organizing and preprocessing data
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Clustering data
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Creating classification models
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Interpreting and evaluating models
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Simplifying data sets
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Using ensembles to improve model performance
Prerequisites
Attended Comprehensive MATLAB or equivalent experience using MATLAB
Partners
Upcoming Program
Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner
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Please keep me posted on the next schedule
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Please contact me to arrange customized/ in-house training
Course Outline
Day 1 of 2
Importing and Organizing Data
Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.
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Data types
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Tables
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Categorical data
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Data preparation
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.
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Unsupervised learning
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Clustering methods
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Cluster evaluation and interpretation
Building Classification Models
Objective: Use supervised learning techniques to perform predictive modelling for classification problems. Evaluate the accuracy of a predictive model.
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Supervised learning
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Training and validation
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Classification methods
Day 2 of 2
Improving Predictive Models
Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.
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Cross validation
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Feature transformation
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Feature selection
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Ensemble learning
Building Regression Models
Objective: Use supervise learning techniques to perform predictive modeling for continuous response variables.
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Parametric regression methods
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Nonparametric regression methods
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Evaluation of regression models
Creating Neural Networks
Objective: Create ad train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
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Clusterng with Self-Organizing Maps
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Classification with feed-forward networks
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Regression with feed-forward networks