Data Processing and Feature Engineering with MATLAB

Data processing and feature engineering with MATLAB are essential steps in any machine learning project. The ability to effectively process and transform data into a format suitable for analysis can have a significant impact on the quality of the results obtained from machine learning models. Feature engineering involves the process of creating new features or variables from existing ones, which can enhance the performance of machine learning models by making them more discriminative and informative. MATLAB is a powerful programming language and environment widely used for data processing and analysis, including feature engineering tasks. This course is designed to provide students with a comprehensive introduction to data processing and feature engineering using MATLAB. The course begins with an overview of the MATLAB environment and basic programming concepts. Students will learn how to use MATLAB to read and write data from different sources, including text files, spreadsheets, and databases. They will also learn how to preprocess data by cleaning, transforming, and normalizing it. The course will cover a range of feature engineering techniques, including dimensionality reduction, feature scaling, feature extraction, and feature selection. Students will learn how to apply these techniques to different types of data, including numerical, categorical, and textual data. The course will include a series of hands-on exercises and projects that will allow students to apply the techniques learned in class to real-world datasets. Students will work on projects such as sentiment analysis, image classification, and time series analysis. They will also learn how to evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, and F1 score. By the end of the course, students will have a solid understanding of the data processing and feature engineering techniques used in machine learning. They will be able to use MATLAB to preprocess data, extract features, and build machine learning models. The skills learned in this course will be valuable for a wide range of applications, including data analysis, predictive modeling, and artificial intelligence. Author: (coursera)