Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Machine Learning with Spark MLlib by Todd McGrath 888 views
- MLlib: Spark's Machine Learning Lib... by jeykottalam 8783 views
- Introduction to Machine Learning wi... by datamantra 5752 views
- Advanced Microservices - Greach 2015 by Steve Pember 1927 views
- Big Data Paris by MapR Technologies 326 views
- Recommendation Techn by Ted Dunning 1334 views

742 views

Published on

This is an excerpt for the Apache Spark with Scala training available at https://www.supergloo.com/fieldnotes/portfolio/apache-spark-scala/

Published in:
Data & Analytics

No Downloads

Total views

742

On SlideShare

0

From Embeds

0

Number of Embeds

36

Shares

0

Downloads

22

Comments

0

Likes

3

No embeds

No notes for slide

- 1. Machine Learning with Spark
- 2. Background • What is machine learning? • Simply put, machine learning is creating and using models that are learned from data. • In other contexts it might be called predictive modeling or data mining.
- 3. More Background • Examples • Predicting whether an email message is spam or not • Predicting whether a credit card transaction is fraudulent • Predicting which advertisement a shopper is most likely to click on • Predicting which product a shopper is likely to purchase; recommendation engines
- 4. Types of Machine Learning Models • Supervised • Unsupervised
- 5. Types of Machine Learning Models • Supervised • Supervised models contains a set of data labeled with the correct answers to learn from • Unsupervised • Unsupervised models do not contain such labels.
- 6. Supervised Machine Learning Models Examples • k-Nearest Neighbors • Imagine trying to predict how a person will vote in the next presidential election. If you know nothing about the person and you are trying to predict their vote, one sensible approach is to look at how their neighbors are planning to vote (if you have the data)
- 7. Supervised Machine Learning Models Examples • Naive Bayes • A common use of the Naive Bayes machine learning model is spam detection. • A key to Naive Bayes is making the (big) assumption that the presences (or absences) of a word are independent of one another, conditional on a message being spam or not
- 8. Supervised Machine Learning Models Examples • Regression Models • Simple Linear Regression - used to prove correlation between two variables • Multiple Regression - used to prove correlation with more than two variables
- 9. Supervised Machine Learning Models Examples • Decision Trees • A decision tree uses a structure to represent a number of possible decision paths and an outcome for each path. • Decision trees are often divided into classiﬁcation trees (which produce categorical outputs) and regression trees (which produce numeric outputs).
- 10. Supervised Machine Learning Models Examples • Neural Networks • Solve problems like handwriting recognition and face detection
- 11. Unsupervised Machine Learning Background • Clustering is an example of unsupervised learning, in which we work with completely unlabeled data. Clustering contrasts with supervised learning which uses labeled data as basis for making predictions • Clusters won’t label themselves. The labels may be determined via unsupervised learning approaches which look at the data underlying each label. • For example, a data set showing where millionaires live probably has clusters in places like Beverly Hills and Manhattan
- 12. Unsupervised Machine Learning Examples • k-means • Number of clusters k is chosen in advance • Example: Clustering may be used when determine or limiting a photograph to only ﬁve colors. For example, you may have a photograph which you want to use to print stickers. However, the printer only supports up to 5 colors per sticker.
- 13. Unsupervised Machine Learning Examples • Latent Dirichlet Analysis (LDA) • Natural Language Processing • Commonly used to identify common topics in a set of documents
- 14. Unsupervised Machine Learning Examples • Network Analysis • Recommender Systems
- 15. For more, visit https://supergloo.com

No public clipboards found for this slide

Be the first to comment