Data Academy Subscription > Machine Learning > Unsupervised Learning in Python
Course Description
In this course, you will learn the basic concepts of unsupervised learning in Python. You will use K-Means Clustering theory to discover the underlying patterns in data. You will assess the performance of clustering using Adjusted Rand Index (ARI) and Confusion Matrix. You will carry out K-means clustering with a rea dataset, use Gaussian Mixture Models to identify clusters, and use hierarchical clustering to categorize data into sets of nested clusters.
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.