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…
Author
Released
8/30/2018- 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
Duration
Views
Related Courses
-
Introduction
-
Course organization1m 17s
-
1. Introduction to Data Science
-
Introduction1m 51s
-
Data science2m 53s
-
Fundamental skills3m 42s
-
Enabling technologies2m 4s
-
-
2. Cloud Computing
-
Cloud fundamentals3m 29s
-
Types of cloud3m 19s
-
Solution providers2m 22s
-
Proxmox: Installation2m 26s
-
3. Distributed File Systems
-
Distributed file systems2m 44s
-
Fundamentals2m 45s
-
Hadoop hands-on2m 8s
-
Hadoop: Preparation4m 11s
-
Hadoop: Installation4m 18s
-
Hadoop: MapReduce hands-on8m 52s
-
-
4. Distributed Processing
-
Spark: Installation6m 24s
-
Spark: Spark shell4m 28s
-
Spark: pyspark4m 32s
-
Spark: Application4m 1s
-
5. Machine Learning
-
Machine learning2m 41s
-
Fundamentals2m 16s
-
Types of machine learning2m 59s
-
Weka: Installation2m 33s
-
Weka: GUI3m 35s
-
Weka: Training vs. testing3m 21s
-
Weka: Clustering2m 12s
-
-
6. Case Study
-
Putting it all together2m 42s
-
Hadoop cluster: Operation4m 57s
-
Spark, YARN, and Hadoop6m 42s
-
Weka and Spark3m 12s
-
-
Conclusion
-
Next steps41s
-
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.
CancelTake notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Fundamentals