Learn about the fundamental applications of machine learning.
- There are several areas…of well-known machine learning applications.…Let's dive into three important ones.…Classification, regression and clustering.…Classification takes a dataset…and divides it into two or more classes.…For classification to work,…a feature model needs to be set up…to assign inputs into one of the classes.…Fraud detection is an example of classification…because it puts a customer transaction…into one of the two classes.…
That is, fraud or legitimate action.…Regression produces a continuous output as opposed…to discrete outcomes like those created by classification.…Regression is used when you are trying…to make a prediction about a phenomenon.…If your sample data shows…that there is a direct positive relationship…between the amount of hours students study…and their final exam scores,…you are able to establish a regression…that can possibly predict a student's score based…on their study hours.…
Clustering divides an input dataset into one or more groups.…This is different from classification…because we don't know what the actual groups are…
- Enabling technologies in data science
- Cloud computing and virtualization
- Installing and working with Proxmox, Hadoop, Spark, and Weka
- Managing virtual machines on Proxmox
- Distributed processing with Spark
- Fundamental applications of machine learning
- Distributed systems and distributed processing
- How Hadoop, Spark, and Weka can work together
Skill Level Beginner
Course organization1m 17s
1. Introduction to Data Science
2. Cloud Computing
3. Distributed File Systems
4. Distributed Processing
5. Machine Learning
6. Case Study
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