Presentation on Machine Learning

1. Client Server Technology
Presented by
Mohit Modyani
Bachelors of Computer Applications – Second
Year
Dezyne E’Cole College, Ajmer

2. Project report on
Machine Learning
Submitted To
Dezyne E’Cole College
Towards
The Partial Fulfillment
Of 2019 Year, Bachelor of Computer Application
By
Mohit Modyani
Dezyne E’Cole College
106/10, Civil Line, Ajmer
www.dezyneecole.com

3. Machine
Learning
Presented by: Mohit Modyani
Bachelor of Compute Application-
2nd Year
www.dezyneecole.com

4. Acknowledgement
I am Mohit Modyani, Student of Bachelor Of
Computer Application, Dezyne E’cole College. I
would like to express my gratitude to each and
every person who has contributed in encouraging
me and helping me to coordinate my project.
I also thank Dezyne E’cole College who
provided insight and expertise that greatly
assisted the project. A special thanks to my
Teachers, Parents and Colleagues who have
supported me at every step. Not to forget, the
Almighty who blessed me with good health
because of which I worked more efficiently and
better.

5. CONTENTS
• What Is Machine Learning ?
• Methods of ML
• Applications of ML
• Challenges of ML
• Future of ML
• Supervised ML
• Application of SML
 Speech Recognition
 Pattern Recognition
 Optical Character Recognition
 Handwriting Recognition
 Spam Recognition
• Unsupervised Learning
• Example of USML
• Semi-Supervised Learning
• Reinforcement Learning
• Conclusion

6. WhAT is ML ?
Machine learning is an
application of artificial
intelligence (AI) that provides
systems the ability to
automatically learn and improve
from experience without being
explicitly programmed.

7. Methods of
ML
Following is an overview of some of
the most accepted ML methods –
1. Supervised Learning
2. Unsupervised Learning
 k-means clustering
 self-organizing maps
 value decomposition
 mapping of nearest
neighbor
3. Semi-supervised Learning
4. Reinforcement Learning

8. Applications of
ML
1. Financial Services
2. Marketing and Sales
3. Government
4. Expert System
5. Healthcare
6. Transportation
7. Oil and Gas
The ML implementation in various
sectors

9. Challegnes of
ML
1. Memory networks
2. Natural language processing
(NLP)
3. Understand deep nets training
4. One-shot learning
5. Deep reinforcement learning
to control robots
6. Semantic segmentation
7. 111
Key challenges of Machine Learning

10. Future of
ML
1. Fine-tuned personalization
2. Better search engine
experiences
3. Evolution of data teams
4. No-code environments
5. Rise of quantum
computing
5 ways it will impact everyday life

11. Supervised
learning
Supervised machine learning (SML) is the machine
learning task of learning a function that maps an input to
an output based on example input-output pairs. It infers
a function from labelled training data consisting of a set
of training examples. In supervised learning, each
example is a pair consisting of an input object (typically
a vector) and a desired output value (also called the
supervisory signal). A supervised learning algorithm
analyzes the training data and produces an inferred
function, which can be used for mapping new examples.
An optimal scenario will allow for the algorithm to
correctly determine the class labels for unseen instances.
This requires the learning algorithm to generalize from
the training data to unseen situations in a "reasonable"
way.

12. Example of
smL
Example:
Classification: Machine is trained to classify
something into some class.
• classifying whether a patient has disease or
not
• classifying whether an email is spam or not
Regression: Machine is trained to predict some
value like price, weight or height.
• predicting house/property price
• predicting stock market price

13. Applications of
smL
The applications of supervised
machine learning
1. Speech Recognition
2. Pattern Recognition
3. Optical Character Recognition
4. Spam Recognition

14. Speech
Recognition
The ability of a machine is to convert
spoken language to text form, it is often
used for voice dialing, call routing and
voice search.
EXAMPLE :
Suppose this person has
done some voice search. So, there are
various software for speech recognition that
convert voice into text form.

15. pattern
Recognition
It is the process of recognizing pattern by using machine
learning algorithm. It classified data based on
knowledge already gained or extracted from patterns.
EXAMPLE :
Nowadays latest smart phones have
fingerprint scanners. So, we first save the pattern of our
fingerprint and hen whenever we try to open our phone
it recognize the pattern which we saved. If the pattern
matches then the phone opens otherwise not.

16. Optical character
Recognition
It converts scanned images to readable text. It is the
mechanical or electric conversion of images of
typed, handwriting into machine encoded text.
EXAMPLE :
According to the image first the
documents are scanned in the form of an image,
then the documents are saved. Next, the OCR takes
place and convert the scanned images into readable
text.

17. handwriting
Recognition
A handwriting recognition system sensor can include a
touch sensitive writing surface or a pen that contains
sensor that detect angle, pressure and direction. The
software then detect the writing pad and translates that
writing in computer.
EXAMPLE :
When we go for license making
there is a touch sensitive writing surface or a pen
that contains sensor. We do our signature on that
surface and it senses the angle, pressure and
direction and translates that signature in computer.

18. sPam
Recognition
It is used to detect unwanted and useless
emails and prevent those message from getting
to users inbox.
EXAMPLE :
Suppose an email was send.
First machine will check that message is
useless or not, if email is useless or unwanted
machine learning sent that email into spam but
if email is wanted and not useless that it will
send to the users inbox.

19. Unsupervised
learning
Unsupervised machine learning (USML) is a
type of self-organized Hebbian learning that
helps find previously unknown patterns in data
set without pre-existing labels. It is also
known as self-organization and allows
modelling probability densities of given
inputs. It is one of the main three categories of
machine learning, along with supervised and
reinforcement learning. Semi-supervised
learning has also been described, and is a
hybridization of supervised and unsupervised
techniques.

20. Example of
usmL
Example:
Clustering: A clustering problem is where you
want to discover the inherent groupings is the data
• such as grouping customers by purchasing
behavior not
Association: An association rule learning problem
is where you want to discover rules that describe
large portion of your data
• such a people that buy X also tend to buy Y

21. Semi-supervised
learning
Semi-supervised machine learning (SSML) is a class of
machine learning tasks and techniques that also make use of
unlabelled data for training – typically a small amount of
labelled data with a large amount of unlabelled data. Semi-
supervised learning falls between unsupervised learning
(without any labelled training data) and supervised learning
(with completely labelled training data). Many machine-
learning researchers have found that unlabelled data, when
used in conjunction with a small amount of labelled data, can
produce considerable improvement in learning accuracy. The
cost associated with the labelling process thus may render a
fully labelled training set infeasible, whereas acquisition of
unlabelled data is relatively inexpensive. In such situations,
semi-supervised learning can be of great practical value.
Semi-supervised learning is also of theoretical interest in
machine learning and as a model for human learning.

22. Reinforcement
learning
Reinforcement learning (RL) is an area of machine
learning concerned with how software agents ought to take
actions in an environment so as to maximize some notion of
cumulative reward. Reinforcement learning is one of three
basic machine learning paradigms, alongside supervised
learning and unsupervised learning.
It differs from supervised learning in that labelled
input/output pairs need not be presented, and sub-optimal
actions need not be explicitly corrected. Instead the focus is
finding a balance between exploration (of uncharted
territory) and exploitation (of current knowledge).[1]
The environment is typically formulated as a Markov
decision process (MDP), as many reinforcement learning
algorithms for this context utilize dynamic programming
techniques.

23. Conclusion of
ML
Machine Learning is a technique of training
machines to perform the activities a human
brain can do.
But bit faster and better than an average
human-being. Today we have seen that the
machines can beat human champions in
games such as Chess, AlphaGO, which are
considered very complex. You have seen that
machines can be trained to perform human
activities in several areas and can aid humans
in living better lives.

24. Thank you
Mohit Modyani
Bachelor of Computer Applications-Second Year
Dezyne E’cole College
www.dezyneecole.com