Machine Learning Specialist - Unsupervised Machine Learning, Time Series and Survival Analysis

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning as well as additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. You will learn how to find and analyze data with a time component and censored data that needs outcome inference.  You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices for unsupervised learning and verifying assumptions derived from Statistical learning.

This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting and those interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.

In order to be successful, you should have knowledge of: To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.