Hello Everyone, The presentation focus on the basics of Machine Learning.

1. GOPAL VERMA

2.  ROBOTICS
 3D PRINTING
 BITCOINS
 VIRTUAL REALITY
 AUTONOMOUS VEHICLES (DRIVERLESS CARS)
 INTERNET OF THINGS (IOT)
 BLOCKCHAIN
 ARTIFICIAL INTELLIGENCE
 MACHINE LEARNING
 DEEP LEARNING

3.  INTRODUCTION
 WHY MACHINE LEARNING
 MACHINE LEARNING APPLICATIONS
 MACHINE LEARNING EXAMPLES
 MACHINE LEARNING MODEL
 CATEGORIES
 USEFUL LINKS

5.  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.
 Learning is any process by which a system
improves performance from experience
 Machine Learning is concerned with computer
programs that automatically improve their
performance through Herbert Simon experience.

6.  Develop systems that can automatically adapt and
customize themselves to individual users –
Personalized news or mail filter
 Discover new knowledge from large databases
(data mining) – Market basket analysis
 Ability to mimic human and replace certain monotonous
tasks which require some intelligence - Recognizing
handwritten characters
 Develop systems that are too difficult/expensive to
construct manually because they require specific
detailed skills or knowledge tuned to a specific task
(knowledge engineering bottleneck)
 Machine learning which helps to determine the real
business outcomes, like saving time and money, which
will naturally going to influence the organization’s future

9.  Machine learning is an important part of these
personal assistants as they collect and refine the
information on the basis of your previous
involvement with them. Later, this set of data is
utilized to render results that are tailored to your
preferences.
 EXAMPLES
SIRI, ALEXA AND GOOGLE

10.  Social media platforms are utilizing machine
learning for personalizing news feed of users for
better ads targeting,
EXAMPLES
 PEOPLE YOU MAY KNOW-
 Facebook suggest a list of users that you can
become friends based on the profiles that you visit
very often, your interests, workplace, or a group
that you share with someone etc
 FACE RECOGNITION
 Facebook recognizes the friend when you upload a
picture of you with a friend based on data such as
poses, projections, unique features . Facebook
matches this data with the people in your friend
list.

11.  Machine learning use a filter to determine whether
an email is spam by looking at its content.
 A filter is trained using a collection of spam and
non-spam(aka “ham”) emails. So, we’ll provide
right answers to train the filter, and later in the
prediction phase, its output for a given message
would be either “spam” or “ham”.
 Some techniques such as multi layer perceptron,
C 4.5 Decision Tree Induction, Naïve Bayes are
used for email spam filtering.

12.  Google and other search engines
use machine learning to improve
the search results for you by
keeping a watch at you queries,
searching, browsing data.

13.  The e-commerce websites recommends you
some items or products based on your taste
using machine learning. The e-commerce
websites recommends products to you based
on your behaviour with the website/application,
past purchase, items liked or added to cart,
brand preference and the like.

14.  Machine learning is proving its potential to
make cyberspace a secure place and
detect online fraud. The companies are
using a set of tools associated with
machine learning which helps them to
compare millions of transactions taking
place and distinguish between legitimate or
illegitimate transactions taking place
between the buyers and sellers.

15.  Spam filtering
 Credit card fraud detection
 Digit recognition on checks, zip codes
 Detecting faces in images
 MRI image analysis
 Recommendation system
 Search engines
 Handwriting recognition
 Scene classification

17. A
• TRAINNING SET
B
• MACHINE LEARNING ALGORITHM
C
• MODEL (F)
Training set is a set of examples used for learning a model
(e.g., a classification model).

21.  Supervised learning, in the context of artificial
intelligence (AI) and machine learning, is a type of
system in which both input and desired output data
are provided. Input and output data are labelled for
classification to provide a learning basis for future
data processing.
 Supervised learning is a method used to enable
machines to classify objects, problems or situations
based on related data fed into the machines.

23.  Suppose you had a basket and it is filled with some
fresh fruits and your task is to arrange the same type
fruits at one place.
 Suppose the fruits are apple, banana, cherry, grape.
 You already know from your previous work that, the
shape of each and every fruit, so it is easy to arrange
the same type of fruits at one place.
 here your previous work is called as train data in
data mining.

24.  so you already learn the things from your train
data, This is because of you have a response
variable which says you that if some fruit have so
and so features it is grape, like that for each and
every fruit.
 This type of data you will get from the train data.
 This type of learning is called as supervised
learning.
 This type solving problem come
under Classification.

26.  Unsupervised learning is the training of an artificial
intelligence (AI) algorithm using information that is
neither classified nor labelled and allowing the
algorithm to act on that information without
guidance.
 Unsupervised learning is a method used to enable
machines to classify both tangible and intangible
objects without providing the machines any prior
information about the objects.

28.  Suppose you had a basket and it is filled with some
fresh fruits your task is to arrange the same type fruits
at one place.
 This time you don't know any thing about that fruits,
you are first time seeing these fruits so how will you
arrange the same type of fruits.
 What you will do first you take on fruit and you will
select any physical character of that particular fruit.
suppose you taken color.
 Then you will arrange them base on the color, then
the groups will be some thing like this.

29.  RED COLOR GROUP: apples & cherry fruits.
 GREEN COLOR GROUP: bananas & grapes.
 so now you will take another physical character as
size, so now the groups will be some thing like this
 RED COLOR AND BIG SIZE: apple.
 RED COLOR AND SMALL SIZE: cherry fruits.
 GREEN COLOR AND BIG SIZE: bananas.
 GREEN COLOR AND SMALL SIZE: grapes.
 Here you didn't know learn any thing before means
no train data and noresponse variable.
 This type of learning is know unsupervised learning.

31.  Reinforcement Learning is a type of Machine
Learning, and thereby also a branch of Artificial
Intelligence. It allows machines and software
agents to automatically determine the ideal
behaviour within a specific context, in order to
maximize its performance.
 The learning method has been adopted in artificial
intelligence (AI) as a method of
directing unsupervised machine learning through
rewards and penalties.

33.  Let’s imagine that a new born baby comes across
a lit candle. Now, the baby does not know what
happens if it touches the flame. Eventually, out of
curiosity, the baby tries to touch the flame and
gets hurt. After this incident, the baby learns that
repeating the same thing again might get him
hurt. So, the next time it sees a burning candle, it
will be more cautious.
 That is exactly how Reinforcement learning
works.

34. 1. PC Games:
Reinforcement learning is widely being used in PC
games like Assasin’s Creed, Chess, etc. where in
the enemies change their moves and approach
based on your performance.
2. Robotics:
Most of the robots that you see in the present world
are running on Reinforcement Learning.
3. AlphaGO:
Go is a Chinese board game which is said to be
more complex than chess. Recently scientists
created a program named ‘AlphaGo’ that competed
with the world champion in this game and won.

35.  http://www.zarantech.com/blog/an-introduction-to-
machine-learning-why-it-matters/
 https://www.youtube.com/watch?v=ukzFI9rgwfU
 https://dataaspirant.wordpress.com/2014/09/19/supervis
ed-and-unsupervised-learning/
 https://www.quora.com/What-is-reinforcement-learning