This course will discuss Stochastic Neighbor Embedding (SNE) and t-Distributed Stochastic Neighbor Embedding (t-SNE) as a means of visualizing high-dimensional datasets. You will learn to implement t-SNE models in scikit-learn and explain the limitations of t-SNE. Being able to abstract high-dimensional information into lower dimensions will prove helpful for visualization and exploratory analysis, as well as in conjunction with the clustering algorithms. By the end of this course, you will be able to find clusters in high-dimensional data, such as user-level information or images.
Previous experience of using Python is required. Prior knowledge of machine learning is not necessary. It's assumed that the learner is aware of the basic mathematical notations and has worked with arrays and dataframes in Python.