This document provides an overview of machine learning presented by Prof. AK Dohre and Asst. Prof. Nidhi Shukla at Rewa Engineering College. It first acknowledges those who provided guidance and support for the presentation. It then defines machine learning and describes the main types: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. It provides data and variable definitions. Python is discussed as a versatile language for machine learning. Key Python constructs like conditional statements, iterative statements, and functions are outlined. Regression and clustering are briefly explained as machine learning techniques.

1. REWA ENGINEERING COLLEGE
PRESENTION
ON
MACHINE LEARNING
SUBMITTED TO:-
SUBMITTED BY :- Proff.AK Dohre
AMAN GOYAL Assistant Proff.Nidhi Shukla
ECE DEPARMENT

2. ACKNOWLEDGEMENT
We are never alone any work and indebted to many people for their reviews,suggestions and technical
discussions.The generation is true for the project as well as,therefore I would like to acknowledge them
here.
First and foremost,I thanks to PROF.A.K. DOHARE(H.O.D. of ECE) for his great supervisions to
develop the text in the shape.
My special thanks to ASST.PROF.NIDHI SHUKLA(Dept of ECE)for their guidance in making the
project work successful.
Also,my gratitude is towards Dr.B.K,AGARWAL(Honourable Principal REC REWA)for giving his advice
and providing us the equipments which was needed.
I appereciate all my colleagues,faculties and my parents for their cooperation in making my work
presentation.
I own a special word of thanks to everyone for their support and encouragement.

4.  Machine learning is the
ability of machine
(computers or ideally
computer program) to
learn from the past
behaviour or data and to
predict the future
outcomes without being
explicity programmed to

6. Types of machine learning

7. Supervised learning model
• Supervised learning model
is the model which learns
among supervision and this
supervision is provided by
labelled data which contains
a target variable and a few
independent variables.

8. Unsupervised learning model
Complement of Supervised
Learning
There is no target variable
involved
Works on only unlabelled data
Identifies if some pattern
exists in the data

9. Semi-supervised learning
As the name suggests,semi-supervised learning is a bit of
both-supervised and unsupervised learning and uses both
labelled and unlabelled data.
This type of learning can be used with methods such as
classification,regression and prediction.
Semi-supervised learning is useful when the cost
associated with labelling is too high to allow for a fully
labelled training process.

10. Reinforcement learning
This model is used in making a sequence of
decisions.It is an learning by interacting with the
environment.
It is based on the observation that intelligent
agents tend to repeat the action that are rewarded
for and retrain from action that are punished for
It can be said that it is an trail and error method in
finding the best outcome based on experience.

11. DATA:-
• Data is any information about something.
• this can be in the form of numbers,text,image,videos and
so on
VARIABLE:-
• A variable represents one specific characteristics of the
data or tells one specific information about the data under
consideration.
• Types of variable:- 1.numeric or quantitive
2.categorical or qualitive

12. Python
Python is a vertsatile language which can be used in web
application,face recognition,object detection etc.
Object oriented programming language which allows to
create and use objects and classes easily.
Compatible with different platforms like windows,mac,etc.
Simple syntax
open source and huge community support

13. Python constructs
1.Conditional statements:-
Syntax:
If condition:
do something
else:
do something else
2. Iterative statements:-
Syntax:
for i in iterable[start:stop:step]:
do something

14. 3.Functions:-
Syntax:
def function_name(input_parameters):
do something
return result
Functions are of two types-
1. Predefined function
2. userdefined function

15. Regression
• Regression is a statistical procedure that determines the
equation for the straight line that best fits a specific set of
data.
• Regression are of two types-
1.Simple linear regression-
 with one dependent and one independent variable.
2.Multiple linear regression-
 with more than one independent and dependent variable.

16. Clustering

17. .
T H A N K Y O U