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Data Processing and Feature Engineering with MATLAB に戻る

MathWorks による Data Processing and Feature Engineering with MATLAB の受講者のレビューおよびフィードバック

4.7
314件の評価

コースについて

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....

人気のレビュー

SK

2020年3月14日

Highly recommended for people with patience, and deep interest in Data Processing and Feature Engineering. It is not easy. However, MATLAB instructional tools make this process so much simplified!

A

2022年1月6日

It was a valuable experience to attend this course. Through its five modules, I acquired a wide range of common and domain specific skills for cleaning and smoothing data and engineering features.

フィルター:

Data Processing and Feature Engineering with MATLAB: 101 - 106 / 106 レビュー

by Myint M M

2020年7月29日

by Amal K

2020年9月18日

by MEHLAM Z

2020年8月11日

by A. M

2020年8月1日

by Mohammad S

2020年7月19日

by RIM B H K

2020年5月2日