Machine Learning Specialist - Supervised Learning: Regression and Classification

1. Introduction to Supervised Machine Learning and Linear Regression

2. Data Splits and Cross Validation

3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

4. Logistic Regression

5. K Nearest Neighbors

6. Support Vector Machines

7. Decision Trees

8. Ensemble Models

9. Modeling Unbalanced Classes

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression and Classification techniques in a business setting.

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: 

Differentiate uses and applications of classification and regression in the context of supervised machine learning. Describe and use linear regression models, and use decision tree and tree-ensemble models. Use a variety of error metrics to compare and select a linear regression model or classification model that best suits your data. Articulate why regularization may help prevent overfitting. Use regularization regressions: Ridge, LASSO, and Elastic net. Use oversampling as techniques to handle unbalanced classes in a data set.