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Machine learning, signal analysis, image analysis, and image processing are some of the most complex and advanced nonspecialized areas of study in science and mathematics. In modern-day practice and research, the techniques and practices from two or more of these areas are often used together. In order to gain comfort working with these modern practices, you have to first gain understanding and experience working with these areas individually. In this MATLAB Advanced course, you will discover how to utilize various MATLAB toolkits, apps, and functions in order to carry out complex and advanced techniques. You’ll be exposed to image processing, image analysis, machine learning, statistical analysis, and much more.
After completing this course, you will be able to apply what you learned to projects involving data science, artificial intelligence, finance, and many other topics. Additionally, you’ll be able to apply these skills in the industry, government, academia, and even personal projects and tasks. Your newfound knowledge and understanding of MATLAB will allow you to use it closer to its full potential.
It covers image processing, image analysis, machine learning, statistical analysis, and more, teaching you how to use various MATLAB toolkits, apps, and functions to carry out complex and advanced techniques.
It is for learners who want to use MATLAB closer to its full potential. The skills can be applied to projects involving data science, artificial intelligence, finance, and many other topics across industry, government, academia, and personal projects and tasks.
The course covers automatic machine learning in MATLAB (AutoML), feature selection, hyperparameter tuning, classification, and regression, including model selection and data model workflows.
You will be able to use machine learning techniques in MATLAB, recognize the difference between signal analysis, image processing, and image analysis, and determine how to execute statistical analysis in MATLAB at various levels.
Yes. It includes signal analysis topics such as the signal analyzer, signal selection, preprocessing, exploration, and analysis sharing, as well as image analysis and processing topics including image segmentation, object analysis, image types, and the Image Processing Toolbox.