Summary
In this chapter, we embarked on an exciting journey into the world of ML, exploring a range of popular algorithms to find the best fit for our specific needs. We learned the importance of conducting a preliminary analysis to determine the most suitable algorithm and gained insights into the step-by-step process of building ML models.
Furthermore, we delved into the powerful capabilities of MATLAB for ML, including its support for classification, regression, clustering, and deep learning tasks. We discovered the convenience of using MATLAB apps for automated model training and code generation, streamlining our workflow.
We also introduced the Statistics and Machine Learning Toolbox and the Deep Learning Toolbox, which provided us with additional tools and functionalities to solve our specific problems. We recognized the significance of statistics and algebra in the field of ML and understood how MATLAB could assist us in leveraging these concepts effectively.
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