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Data Processing and Feature Engineering with MATLAB


What you'll learn

Prepare data for further analysis by removing noise, identifying outliers, & merging data from multiple sources

Create and evaluate features for machine learning applications

Explore special techniques for handling textual, audio, & image data

Perform unsupervised machine learning

There are 5 modules in this course

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling.  This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling.  

These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed Exploratory Data Analysis with MATLAB. 

Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data.  In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling.   By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions.  You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.

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