Presented at the Dubai Data Science Meetup on May 5, 2018

5. What is Machine Learning
D E F I N I T I O N S
“The field of study that gives computers/machines the ability to learn
without being explicitly programmed” - Arthur Samuel
“A computer program is said to learn from experience E with respect to
some class of tasks T and performance measure P, if its performance at
tasks in T, as measured by P, improves with experience E ” – Tom Mitchel

6. Classification
of Machine
Learning
Question : Why is this
important?

7. Supervised
Learning
Regression
• Used for continuous value responses
• When the output is not bound to a defined set of values
• Predicting the price of a house given the size of the house in Sq. feet
Classification
• Used for categorical response values , where the data can be separated
into specific “classes”.
• When the output is bound to a discrete set of values.
• Should an incoming email go to the inbox / spam folder

8. Regression Vs. Classification
Linear Regression : Algorithm used to predict continuous values
Logistic Regression : Algorithm to predict outputs that belong to predefined classes

9. Unsupervised
Learning
Clustering
• Google news , where news of the same topics are grouped in one category
• Deriving structure to Chaotic data ; or unstructured data
Non-Clustering
• Getting a particular data out of a set of unstructured data
• Removing the voice samples of males that are of age above 25 from voice
samples collected at a cocktail party

10. What is Deep Learning then?
• Deep Learning is a subset of Machine
Learning
• Deep Learning is a method to solve
Supervised Machine Learning Problems

11. Neural Network : Fundamentals of Deep Learning

12. Neural Network :
Fundamentals of
Deep Learning

14. Deep Learning
Frameworks
“Deep learning
framework offers
building blocks for
designing, training
and validating deep
neural network
through a high-level
programming
interface”
A Tensor Object
Operations on the Tensor
Object
A Computational Graph
Optimizations

15. AI Platform

16. process
identify
explore
analyze
encode model
predict

17. We always
start with a
sharp question

18. What is your question?

19. So how can you solve a question

20. So how can you solve a question

21. Apps + insights
Social
LOB
Graph
IoT
Image
CRM INGEST STORE PREP & TRAIN MODEL & SERVE
Data orchestration
and monitoring
Data lake
and storage
Hadoop/Spark/SQL
and ML IoT
Azure Machine Learning
Microsoft Solutions to solving a ML problem

22. Data Wrangling/ Data Preparation/Data Cleaning
• Advanced Analytics
• Visualization
• Observation
• Statistics

23. Choosing an Algorithm

24. Evaluating a model
• A model is created based on the training data and then tested against a Test
data
• Divide the data into 2 sets
• Training Data
• Test Data
• How good is a model against the real data? – Scoring the model against the
test data ; y_predict Vs. y_test
• What is the accuracy of the prediction?

25. Deploying a model
• Deploying a model includes creating a web service that your applications can
consume
• This can be achieve by the model management service

26. Azure Machine Learning
VISUAL DRAG-AND-DROP CODE-FIRST

27. Azure Machine Learning
Studio
Platform for emerging data scientists to
graphically build and deploy experiments
• Rapid experiment composition
• > 100 easily configured modules for data
prep, training, evaluation
• Extensibility through R & Python
• Serverless training and deployment
Some numbers
• 100’s of thousands of deployed models
serving billions of requests