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image for course Machine Learning Onramp

Machine Learning Onramp

Learn the basics of practical machine learning for classification problems in MATLAB®. Use a machine learning model that extracts information from real-world data to group your data into predefined categories.

Course modules

Overview of Machine Learning

Familiarize yourself with machine learning concepts and the course.

Lessons:
  • What is Machine Learning
  • Train Machine Learning Models in Classification Learner
  • Machine Learning Workflow

Import Data

Import data into MATLAB.

Lessons:
  • Import a Letter
  • Organization of Data Files
  • Import Multiple Letters

Extract Features

Extract features from tabular data.

Lessons:
  • What are Features?
  • Extract Letter Features
  • Create a Function to Extract Features
  • Extract Features from Multiple Files

Split Data for Validation

Use test and validation sets to evaluate model performance.

Lessons:
  • Training Data vs. Testing Data
  • Set Training and Testing Data in Classification Learner
  • What is Cross-Validation?
  • Set Cross-Validation in Classification Learner

Train Models

Train classification models.

Lessons:
  • How Classification Methods Work
  • Train Models in Classification Learner
  • Change Model Hyperparameters

Evaluate Performance

Use different methods to evaluate model performance.

Lessons:
  • Compare Models
  • Confusion Matrix

Improve Performance

Identify possible ways to improve model performance.

Lessons:
  • Considerations for Improving Performance

Conclusion

Learn next steps and give feedback on the course.

Lessons:
  • More Machine Learning Applications
  • Additional Resources
  • Survey

Format:Self-paced

Language:English

Language

  • Hands-on exercises with automated feedback
  • Access to MATLAB through your web browser
  • Shareable progress report and course certificate

Deep Learning Onramp

Get started quickly using deep learning methods to perform image recognition.

MATLAB Onramp

Get started quickly with the basics of MATLAB.

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