# Course details

Machine & Deep Learning Algorithms: Regression & Clustering

### Machine & Deep Learning Algorithms: Regression & Clustering

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description

In this 8-video course, explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models. Begin by examining application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model. Then study an introduction to regression and how it works. Next, take a look at the characteristics of regression such as simplicity and versatility, which have led to widespread adoption of this technique in a number of different fields. Learn to distinguish between supervised learning techniques such as regression and classifications, and unsupervised learning methods such as clustering. You will look at how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data. Recognize the need to reduce large data sets with many features into a handful of principal components with the PCA (Principal Component Analysis) technique. Finally, conclude the course with an exercise recalling concepts such as precision and recall, and use cases for unsupervised learning.

Expected Duration (hours)
0.8

Lesson Objectives

Machine & Deep Learning Algorithms: Regression & Clustering

• Course Overview
• recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model
• describe how regression works by finding the best fit straight line to model the relationships in your data
• list the characteristics of regression such as simplicity and versatility, which have led to the widespread adoption of this technique in a number of different fields
• distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering
• describe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data
• recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique
• to recall concepts such as precision and recall and the use cases for unsupervised learning
• Course Number: