Machine Learning Basics - What is Machine Learning - Types of Machine Learning - Some Machine Learning Algorithms - Use Cases of Machine Learning

1. Machine Learning
Simplified

2. What we will
discuss?
What is
Machine
Learning?
Why it is a
hype now-a-
days?
Machine
Learning
paradigms
and some
algorithms.
• Regressions
• DecisionTrees
• SVMs
• Neural
Networks
Use cases
of ML

3. What is
Machine
Learning?
• Machine learning is a field of computer science. It is also a type of
Artificial Intelligence that enables the programmers to write
programs in a more simple way. It focuses more on developing
programs that teach computers to change when exposed to new
data and to grow. Its goal is to understand and follow the methods
by using algorithms to do that task automatically without any
human assistance.
• In 1959, Arthur Samuel defined machine learning as a
"Field of study that gives computers the ability to learn without being
explicitly programmed".

4. Wooaaahh!
That's way too much to mug up !!!

5. Learn from Experience
Learn from Instructions
Learn from Experience
Using ML

6. ButWhy to learn this Machine Learning stuffs?

7. Simply Because It is
being used so Extensively!
• The heavily hyped, self-driving Google car? The essence of machine learning.
• Online recommendation offers such as those from Amazon and Netflix?
Machine learning applications for everyday life.
• Sentiment Analysis : Knowing what customers are saying about you on
Twitter? Machine learning combined with linguistic rule creation.
• Fraud detection? One of the more obvious, important uses in our world today.

8. And in lots and lots many fields that we can't even imagine!
So Let's Dig in Quickly into some Machine
Learning Techniques and Paradigms

9. Machine Learning Paradigms
1. Supervised Machine Learning
2. Unsupervised Machine Learning
3. Reinforcement Learning
4. Semi-Supervised Machine Learning

10. Supervised Learning

11. How Supervised Learning
Works!

12. Supervised
Learning
 In supervised learning, the system tries to learn from the previous
examples that are given.
 Speaking mathematically, supervised learning is where we have
both input variables (x) and output variables(Y) and can use an
algorithm to derive the mapping function from the input to the
output.
 The mapping function is expressed asY = f(X).

13. Supervised
Learning
Example
1. Hand Written Digit
Recognition
2. Spam Detection

14. Supervised
Learning
further
classified into
two parts!
• A classification problem is
when the output variable
is a category or a group,
such as “black” or “white”
or “spam” and “no spam”.
Classification
• A regression problem is
when the output variable
is a real value, such as
“Rupees” or “height.”
Regression

15. Unsupervised Learning

16. Unsupervised
Learning
 The model learns through observation and finds structures in the
data.
 Once the model is given a dataset, it automatically finds patterns
and relationships in the dataset by creating clusters in it.
 Suppose we presented images of apples, bananas and mangoes to
the model, so what it does, based on some patterns and
relationships it creates clusters and divides the dataset into those
clusters. Now if a new data is fed to the model, it adds it to one of
the created clusters.

17. Unsupervised
Learning
further divided
into
• An association rule learning
problem is where we want to
discover rules that describe large
portions of your data, such as
“people that buy X also tend to buy
Y”.
• E.g. Amazon, Netflix etc.
Association
• A clustering problem is where you
want to discover the inherent
groupings in the data, such as
grouping customers by purchasing
behavior.
• E.g. Music Recommendations, etc.
Clustering

18. Reinforcement
Learning

19. Reinforcement
Learning
 It is the ability of an agent to interact with the environment and
find out what is the best outcome. It follows the concept of hit and
trial method.The agent is rewarded or penalized with a point for a
correct or a wrong answer, and on the basis of the positive reward
points gained the model trains itself.And again once trained it
gets ready to predict the new data presented to it.
 This technique is being used in many robotic systems like,
1. Self Driving Cars
2. Space Rovers
3. SurveillanceQuadcopters etc.

20. Some Machine Learning Algorithms

21. Use Cases of
Machine Learning

22. 1. Virtual Personal Assistants

23. 2. Computer Vision
Face Detection
Features Detection

24. 3. Recommender Systems
Netflix
Recommendations
Amazon
Recommendations

25. 4. Natural Language Processing
Sentiment Analysis Spam/Malware Detection

26. 5. Identifying Cheaters
Counter Strike Global Offensive community
game uses Machine Learning to identify
cheaters and hackers in game.