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

1. Introduction to Unsupervised Learning and K Means

2. Selecting a clustering algorithm

3. Dimensionality Reduction

4. Introduction to Time Series Analysis

5. Stationarity and Time Series Smoothing

6. ARMA and ARIMA Models

7. Deep Learning and Survival Analysis Forecasts

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.

By the end of this course you should be able to: 

Explain the kinds of problems suitable for Unsupervised Learning approaches. Explain the curse of dimensionality, and how it makes clustering difficult with many features. Describe and use common clustering and dimensionality-reduction algorithms. Try clustering points where appropriate, compare the performance of per-cluster models. Understand metrics relevant for characterizing clusters. Identify common modeling challenges with time series data. Explain how to decompose Time Series data: trend, seasonality, and residuals. Explain how autoregressive, moving average, and ARIMA models work. Understand how to select and implement various Time Series models. Describe hazard and survival modeling approaches. Identify types of problems suitable for survival analysis.