Machine learning allows machines to learn from examples and experience to progressively improve performance on tasks. It involves using algorithms and statistical models to analyze experience in the form of data. There are three main types of machine learning: supervised learning which uses labeled input and output data to train a model; unsupervised learning which finds patterns in unlabeled data; and reinforcement learning where an agent learns through trial-and-error interactions with an environment.

2. INTRODUCTION OF MACHINE
LEARNING
• Machine learning is a concept which allows
the machine to learn from examples and
experience.
• It is the scientific study of algorithm and
statistical models that computer system used
to progressively improved their performance
on specific task.
• It is a technique to produce output with
experience.

3. Definition of machine learning by mitchell
A computer program is said to learn from
experience E with respect to some class of
task T and performance measure P if its
performance on task T as measured by P
improves with experience E.
• E - data
• T – task, prediction ,class
• P -output

4. Example of machine learning
• Spam filtering :- identify email messages as
spam or non spam.
• Medical diagnosis:- diagnosis a patient as a
sufferer or non sufferer of some diseases.
• Fraud detection:- identify credit card
transactions which may be fraudulent in
nature.
• Weather prediction :- predict for instance
whether or not it will rain tomorrow .

9. • Supervised Learning – Train Me!
• Unsupervised Learning – I am self sufficient
in learning
• Reinforcement Learning – My life My rules!
(Hit & Trial)

10. Types of machine learning
1. Supervised machine learning
Labels and features both are included in a
training set of supervised machine learning
on the basis of training instances system will
generate a model & provide the output for
new similar problem or task. supervised
learning is also known as task driving learning
. There are a number of inputs & outputs
which help to generate a model.

11. Unsupervised machine learning
• The model learns through observation and
find structure in the data . Once the model is
given the data set it automatically find
patterns and relationship in the dataset by
creating clusters in it.
• it is an approach to machine learning where
by software learn from data without being
given correct answer.

12. reinforcement machine 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.

13. Examples of reinforcement learning
• Game playing: Let's consider a board game like Go or Chess. In
order to determine the best move, the players need to think about
various factors. The number of possibilities is so large that it is not
possible to perform a brute-force search. If we were to build a
machine to play such a game using traditional techniques, we need
to specify a large number of rules to cover all these possibilities.
Reinforcement learning completely bypasses this problem. We do
not need to manually specify any rules. The learning agent simply
learns by actually playing the game.
• Robotics: Let's consider a robot whose job is to explore a new
building. It has to make sure it has enough power left to come back
to the base station. This robot has to decide if it should make
decisions by considering...
•

14. Steps of reinforcement learning
Step 1-input state is observed by the agent
Step 2-Decision making function is used to
make the agent to perform an action
Step 3-After the action is performed the agent
receive the reward from the environment.
Step 4-The state action pair information about
the reward is stored.