In this presentation I show a brief introduction to Machine Learning and its applications. I also present two cloud platforms for Machine Learning: Microsoft Azure for Machine Learning and MonkeyLearn.

1. Machine Learning: Artificial
Intelligence isn't just a Science
Fiction topic
Raul Garreta - Tryolabs / MonkeyLearn

2. My Credentials
● Computer Science Engineer from Udelar,
Msc in Machine Learning + NLP
● Co-Founder, CTO & Product Manager at
Tryolabs.
● Co-Founder at MonkeyLearn.
● Professor in ML at InCo, Udelar.
● Co-authored "Learning Scikit-learn:
Machine Learning in Python"

3. Contents
● Brief intro to AI & Machine Learning (ML)
● ML Applications
● Cloud ML tools

4. What is AI?
From a behavioral point of view, is an artificial
agent that shows certain characteristics of
intelligence like:
● Reasoning
● Knowledge representation
● Learning
● Planning
● Perception

5. What is AI?
Behavioral test = Turing Test
If I write an enough complex If-
then-else structure, could it
pass the test?
Random behavior?

6. Different fields within AI
Artificial Intelligence
● General Artificial Intelligence
● Expert Systems
○ Natural Language Processing
○ Computer Vision
○ Machine Learning
○ ...

7. Machine Learning
Algorithms that allow computers
to automatically learn to perform
a task from data.
Can improve their performance
over time, by adding more data.

8. Machine Learning Definitions
Arthur Samuel (1959): "Field of study that gives computers
the ability to learn without being explicitly programmed"
Tom Mitchell (1997): "A computer program is said to learn
if its performance at a task T, as measured by a
performance P, improves with experience E"

9. Machine Learning Algorithms
● Learn to associate a particular input (set of
features) to a particular output (class,
number or group of instances)
● That is the process of training a ML model.
● And use the learned model to predict the
outcome on new instances

10. Inputs: Instances
Usually we have instances of data that
represent objects: documents, images, users,
etc.
And can be represented by a set of features:
● A document is represented by a set of words.
● An image is represented by a set of pixels.
● A user can be represented by the age, level of
education, gender, interests, etc.

11. Machine Learning Problems
Classification: assign a label (class)
to a set of items.
Regression: assign a number
(evaluation) to a set of items
Clustering: group items into clusters
according to a similarity measure

12. Type of Machine Learning
Algorithms
Decision TreesLinear Models

13. Type of Machine Learning
Algorithms
Probabilistic /
Statistical Models
Neural Networks /
Deep Learning

15. Important Concepts in ML
Besides the Machine Learning…
● Data gathering / importation
● Data preprocessing
● Feature extraction
● Feature selection
● Performance evaluation (testing)

16. Applications
Natural Language Processing
Text Mining Speech to Text

17. Applications:
Computer Vision
Face Recognition OCR

18. Applications
Data Mining / Predictive Analytics
Recommendation Engines Medicine

19. Applications
Intelligent Agents
Robotics Game Players

20. Why use Machine Learning?
● Solve problems that manually would be extremely
difficult or impossible.
● Make predictions.
● Automatically process huge amounts of information and
sources: big data.
● Intelligent apps => improve UX => improve conversion
rates => $$$
● Great companies use it...

21. ● Avoid to deploy and maintain the full stack.
● Be cross platform.
● Not all programming languages have ML
tools.
● ML requires huge amounts of computer
power.
● Just solve it: good, fast, easy.
Why use a Cloud Saas ML platform?

22. As with other problems (eg: payments,
communications) is a trend to go SaaS.
Machine Learning Platforms
Machine Learning

23. Microsoft Azure ML
● http://azure.microsoft.com/en-
us/services/machine-learning/
● Launched preview version on June 2014.
● Cloud based ML platform to build predictive
numerical applications.
● Technologies used in Xbox and Bing.
Machine Learning

24. Microsoft Azure ML
● Easy to scale, Azure infrastructure.
● Users can build custom R modules.
● GUI and APIs.
● More oriented to Data Scientists.
● Pricing: pay as you go.
Machine Learning

26. MonkeyLearn
● http://monkeylearn.com/
● Launched private alpha on April 2014
● Cloud based, focused on Text Mining:
extract and classify information from text.

27. MonkeyLearn
● Easy to use.
● Pre-trained modules for different
applications.
● GUI and APIs.
● More oriented to developers.
● Pricing: freemium, pay as you go.

29. Conclusions
● Machine Learning can allow
us to make intelligent apps.
● It's a trendy topic…
● New ML platforms are
emerging, allowing any
developer to incorporate ML
technologies.