Machine Learning Made Easy

MATLAB files from the webinar
Updated 1 Sep 2016

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These files accompany the 'Machine Learning Made Easy' webinar which can be viewed here:
About the webinar:
Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
In this session we explore the fundamentals of machine learning using MATLAB®.
Highlights include:
• Accessing, exploring, analyzing, and visualizing data in MATLAB
• Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox® to perform common machine learning tasks such as:
o Feature selection and feature transformation
o Specifying cross-validation schemes
o Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
o Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data
• Integrating trained models into applications such as computer vision, signal processing, and data analytics.

Cite As

Shashank Prasanna (2024). Machine Learning Made Easy (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2015a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

Updated license

Fixed the webinar video link

Added link to the webinar recording

Updated required products list

Updated required products

Updated formatting in the published script