There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models to make predictions, unsupervised learning finds patterns in unlabeled data through clustering, and reinforcement learning allows agents to learn behaviors through rewards and punishments from its environment.

1. Types of Machine Learning
Himani

2. Topics to be cover
Supervised,
Unsupervised,
Reinforcement

3. Types of Machine Learning
Supervised
Learning – Train
Me!
Reinforcement
Learning – My
life My rules!
(Hit & Trial)
Unsupervised
Learning – I am
self sufficient in
learning

4. Supervised
and
unsupervised
are mostly used by
a lot machine
learning engineers
and data geeks
Reinforcement
learning
is really powerful
and complex to
apply for problems.

5. Supervised Learning
• A model is able to make the
predictions based on past data.
• It is the easiest to understand and the
simplest to implement.
• It is mainly used in real world
applications.

6. Supervised
Learning is
the one,
where you
can consider
the learning
is guided by a
teacher.
We have a
dataset which
acts as a
teacher and
its role is to
train the
model or the
machine.
Once the
model gets
trained it can
start making
a prediction
or decision
when new
data is given
to it.

7. Known Data
These are
stars
Known
Response
New Data
Model
It is a
Star
Output
Inputs
How it works?

8. Examples:
 Teacher teaches you
numbers
 Face Recognition
 Product Recommendations
 Advertisement Popularity
 Spam Classification

9. Unsupervised Learning
• Unsupervised learning is very much the opposite of
supervised learning.
• The training data does not include Targets here so we don’t
tell the system where to go , the system has to understand
itself from the data we give.

10. 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.
It cannot
do is add
labels to
the cluster,

11. Input
Model
Output
How it works?
Understand
the patterns
and data
itself

12. Examples:
 Recommender Systems
(YouTube, Netflix etc)
 Buying Habits
 Grouping User Logs

13. Reinforcement Learning
• It is about taking suitable action to maximize
reward in a particular situation.
• It is employed by various software and
machines to find the best possible behaviour
or path it should take in a specific situation.

14. 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.

15. How it works?

16. Examples:
 Video Games
 Industrial Simulation
 Resource Management

18. Thank
You