Presented by Ms. Nandan SV Team Lead, Sr Software Developer, Livares Technologies

1. INTRODUCTION TO
MACHINE LEARNING
Presented By
Nandana SV Livares Technologies Pvt LtdTech & Socio-Cultural Group

2. How does Machine Learning Work?

3. How does Machine Learning Work?

5. Let’s see an example with Songs

6. Let’s plot a graph with two features

7. We can group his taste for songs.

8. When there is a new song ‘A’ in town

9. ● The system will be able to recognize if he likes this or not .
● It does not need ML.

10. When there is another song ‘B’ which cannot be
included in the group.

11. We use algorithms to find if he would like it or not

12. Data is the most important requirement for ML

16. Let’s take the case of cricket

23. Scenario 1: Facebook recognizes your friend in a
picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to
recognize the person. Therefore, the tagged photos become the labels of the
pictures and we know that when the machine is learning from labeled data, it is
supervised learning.

24. Scenario 2: Recommending new songs based on
someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-
existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the
songs/movies that you like already, evaluate the features based on your
likes/dislikes and then recommend new movies/songs based on similar
features.

25. Scenario 3: Analyze bank data for suspicious-
looking transactions and flag the fraud
transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions
are not defined, hence there are no labels of "fraud" and "not fraud". The model
tries to identify outliers by looking at anomalous transactions and flags them as
'fraud'.

26. Why has ML become such a hype now?
ENORMOUS
AMOUNT OF DATA

28. Uber is one application which uses ML

31. Predictive Modeling

32. Can someone say who
this is?

33. THIS PERSON DOES NOT EXIST
1. The site is the creation of Philip Wang, a software engineer at Uber, and uses
research released last year by chip designer Nvidia to create an endless
stream of fake portraits.
2. Generative adversarial network
3. The underlying AI framework powering the site was originally invented by a
researcher named Ian Goodfellow
4. Although this version of the model is trained to generate human faces, it can,
in theory, mimic any source.
5. Researchers are already experimenting with other targets including anime
characters, fonts, and graffiti.

34. GAN
5. Programs like this could create endless virtual worlds, as well as help
designers and illustrators. They’re already leading to new types of artwork.
6. The GANfather , Ian Goodfellow: The man who’s given machines the gift of
imagination
7. Researchers were already using neural networks, algorithms loosely modeled
on the web of neurons in the human brain, as “generative” models to create
plausible new data of their own.
8. But the results were often not very good: images of a computer-generated
face tended to be blurry or have errors like missing ears.

35. 9. The plan Goodfellow’s friends were proposing was to use a complex
statistical analysis of the elements that make up a photograph to help
machines come up with images by themselves. This would have required a
massive amount of number-crunching.
10.He Pitted two neural networks against each other?
11.The goal of GANs is to give machines something akin to an imagination.
12.That will mark a big leap forward in what’s known in AI as “unsupervised
learning.” A self-driving car could teach itself about many different road
conditions without leaving the garage.
13.A robot could anticipate the obstacles it might encounter
in a busy warehouse without needing to be taken around it.
GAN

36. 13.The magic of GANs lies in the rivalry between the two neural nets.
14.It mimics the back-and-forth between a picture forger and an art detective
who repeatedly try to outwit one another.
15.Both networks are trained on the same data set.
16.The first one, known as the generator, is charged with producing artificial
outputs, such as photos or handwriting, that are as realistic as possible.
17.The second, known as the discriminator, compares these with genuine
images from the original data set and tries to determine which are real and
which are fake.
GAN

37. 18.On the basis of those results, the generator adjusts its parameters for
creating new images.
19.And so it goes, until the discriminator can no longer tell what’s genuine and
what’s bogus.
20.Privacy concerns mean researchers sometimes can’t get enough real patient
data to, say, analyze why a drug didn’t work.GANs can help solve this problem
by generating fake records that are almost as good as the real thing
GAN

38. CAN YOU GIVE SOME EXAMPLES FROM
EVERYDAY LIFE WHERE MACHINE
LEARNING HAS DONE OR COULD DO
AMAZING JOB?

39. OUR
CONTACT DETAILS
Livares Technologies Pvt Ltd
5th Floor, Yamuna Building
Technopark Phase III Campus
Trivandrum, Kerala, India-695581
Livares Technologies Pvt LtdTech&Socio-Cultural Group
Our helpline is always open to receive any inquiry
or feedback.Please feel free to contact us
www.livares.com
contact@livares.com
@livaresofficial
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+91-471-2710003 | +91-471-2710004
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