The document discusses the use of machine learning (ML) and artificial intelligence (AI) in building smart applications that simulate intellectual tasks. It outlines the different types of analytics: descriptive, predictive, and prescriptive, and emphasizes the role of supervised machine learning algorithms in predictive analytics. Additionally, it highlights the importance of data collection, preparation, and model evaluation in the machine learning process.

1. … build smart applications, quickly

5. To simulate any
intellectual task
One of the most successful
approaches to AI, that uses
statistical techniques over
data to mimic intelligence
Subfield of ML concerned
with algorithms inspired by
artificial neural networks

6. Descriptive
What has
happened?
Predictive
What could
happen?
Prescriptive
What should
we do?
Insight into the past Understanding the future Advise / Suggestions

7. Descriptive
What has
happened?
Predictive
What could
happen?
Prescriptive
What should
we do?
Supervised machine learning algorithms can be used for predictive analytics
Not all predictive analytics need to be done using Machine Learning approaches

8. Clear
Objective
Data
Collection
Data
Preparation
Training
Model(s)
Evaluate
Model(s)
Publish
model

9. Image Courtesy : Introducing Azure ML by David Chappell

11. ML Studio Data Preprocessing Modules Machine Learning Algorithms Ease of Deployment

12. Image Courtesy : Introducing Azure ML by David Chappell

19. aum2deepak@gmail.com
deshevan@microsoft.com