This document discusses machine learning and provides examples of common machine learning algorithms. It begins with definitions of machine learning and the machine learning process. It then describes four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and discusses five common algorithms - K-nearest neighbors, linear regression, decision trees, naive Bayes, and support vector machines. It concludes with an overview of a heart disease prediction mini-project using Python.

1. JainAGMInstitute Of Technology Jamkhandi
Machine Learning
A internship submitted in partial fulfilment of the requirements
for the degree of
BACHELOR OF TECHNOLOGY in ELECTRONICS AND
COMMUNICATION ENGINEERING
Savita L Hanchinal
USN-2JG17EC004
Department of Electronics and
Communication Engineering
Under the guidance of
Mr. Allayya Kudli
Assistant Professor

3. 1.What Is Machine Learning?
Overview
2.Machine Learning Process
3.Types Of Machine Learning
4.Machine Learning Algorithms
5.Mini Project(Heart Disease
Prediction) Using Python
5
4
3
2
1

4. What is Machine Learning?
Machine Learning is an application of Artificial Intelligence
(AI) that provides system the ability to automatically learn and
improve from experience without being explicitly programmed
Ordinary System With Artificial
Intelligence
Machine Learning
Predicts
Improves

5. Machine Learning Process
Input Data
Analyse Data
Find Pattern
Prediction/Decision
Learns from the
feedback

6. Types In Machine Learning
The machine learns
from the training
data that is labelled
No labelled
training data
Machine learns on
it’s own

7. Types In Machine Learning
1.Supervised Machine Learning
Machine Learning
Model

8. Types In Machine Learning
2.Unsupervised Machine Learning
Input Data Model Output

9. Types In Machine Learning
2.Reinforcemet Machine Learning

10. Types In Machine Learning

11. Machine Learning Algorithm

12. K-Nearest neighbours
 K Nearest Neighbour is a simple algorithm that stores all the available cases and
classifies the new data or case based on a similarity measure. It is mostly used to
classifies a data point based on how its neighbours are classified.
 In K Nearest neighbour K can be a integer greater than 1.So ,far every new data
point we want to classify, we compute to which neighbouring group it is closest.

13. K-Nearest neighbours Example
cost
Durability
Consider there are three clusters
Football
Basket ball
Tennis ball
Now we have anew data point
(blackball)
We try to classify using KNN
Here lets us take K=8
Here the majority of balls within
circle are tennis ball
So the unknown ball is classified as tennis ball
Draw a circle

14. Linear Regression
 Linear regression attempts to model the relationship between two variables by
fitting a linear equation to observed data. One variable is considered to be an
explanatory variable, and the other is considered to be a dependent variable. For
example, a modeler might want to relate the weights of individuals to their
heights using a linear regression model.

15. Linear Regression
The regression line(green line) has the least value of D
D=d12+d22+d32+d42+d52+d62+d72+d82+d92
d1
d2
d3
d4
d5 d6
d8
d7
d9
X
Y

16. Decision Tree
Decision Tree : Decision tree is the most powerful and popular tool for
classification and prediction. A Decision tree is a flowchart like tree structure,
where each internal node denotes a test on an attribute, each branch represents an
outcome of the test, and each leaf node (terminal node) holds a class label.

17. Decision Tree Example
Is it sunny?
Go Swim Is it raining?
Stay Indoor Walk the dog
Yes
Yes
NO
No

18. Naive Bayes
The Naive Bayes Classifier technique is based on conditional probability and is
practically suited when the complexity of inputs is high
Receives Mail
Old spam mail records
Applying Bayes Theorem
Predicts weather a mail is
spam

19. Naive Bayes Example

20. Support Vector Machine(SVM)
X
Y
Hyperplane
Marginal Plane

21. Heart Disease Prediction
Using Python

31. Advantages
1. Automation of Everything
2. Wide Range of Applications
3. Scope of Improvement
4. Efficient Handling of Data
5. Best for Education and Online Shopping
Disadvantages
1. Possibility of High Error
2. Algorithm Selection
3. Data Acquisition
4. Time and Space

32. Applications
1. Image Recognisation
2. Speech Recognisation
3. Product Recomandation
4. Virtual Personal assistance
5. Medical diagnosis
6. Automatic language translation