Machine Learning Made Easy
Updated 1 Sep 2016
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®.
• 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.
Shashank Prasanna (2023). Machine Learning Made Easy (https://www.mathworks.com/matlabcentral/fileexchange/50232-machine-learning-made-easy), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox >
- Test and Measurement > WSNs >
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