Data Academy Subscription > Machine Learning > Dimension Reduction & Feature Selection for Machine Learning
Course Description
In this course, you will learn the foundation of dimension reduction and feature selection for machine learning. You will apply principal component analysis (PCA) and explore Linear Discriminant Analysis (LDA) and t-SNE dimension reduction. You will learn how select the most important features and those with the strongest relationships with the output variables using univariate selection.
Requirements
  • A basic knowledge of common machine learning terminology is and statistics terminology helpful, though not required for this course.
Instructor:
Minerva Singh
Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.