Machine learning is a form of artificial intelligence that allows computers to learn without being explicitly programmed by exploring data, identifying patterns, and improving with experience. The document outlines the history of machine learning and how it works, describing different types including supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. It also discusses applications and advantages/disadvantages of machine learning.

5. What is
Machine
Learning?

6. “Fieldofstudythatgivescomputerstheabilitytolearnwithout
beingexplicitlyprogrammed”

7. History of
Machine
Learning

8. IN 1943
Mathematical
Neural networking
modeling:
Walter Pitts and
Warren McCulloh
Published first
mathematical modeling
of a Neural Network.
IN 1950 The Tuning Test:
Alan Turing Published
an article answering
the questions CAN
MACHINES THINK?
IN 1952
Arthur Samuel's
Cheekers program:
A program for IBM
computer.
IN 1956
Research project in
Dartmouth College:
John McCarthy
invited
mathematicians for
Six to eight weeks
event about AI.

9. How
Machine
Learning
works?

10. TRADITIONAL
PROGRAMMING
DATA (INPUT)
PROGRAM
MACHINE
LEARNING
DATA(INPUT)
OUTPUT
PROGRAM
OUTPUT
PROGRAM

11.  Machine learning is a form of Artificial Intelligence that teaches
computer to think in a similar way to how humans do: learning and
improving upon past experiences. It works by exploring data,
identifying patterns, and invovles minimal human intervention
Gathering
data
Preparing
data
Train Mode
Test Data
Improve

13. Types of
Machine
Learning

14. Supervised Learning
Unsupervised
Learning
Reinforcement
Learning
Semi-Supervised
learning

15. Supervised
Learning

16.  In this type, the model is trained on a labeled dataset,
meaning that each example in the training data has a
known label. The model can then make predictions on
new, unlabeled data
INPUT FUNTION
OUTPU
T

17. Labeled Data
Labels
HEXAGON
TRIANGL
E
SQUAR
E
Model Training
Prediction
Test Data
Square
Triangle
WORKING
MODEL

18. Types of
Supervised
Learning
Classification
Regression

19.  Classification problems use an algorithm to accurately
assign test data into specific categories, such as separating
apples from oranges or to classify spam in a separate folder
from your inbox by supervised algorithm
 Linear classifiers, support vector machines, decision trees
and random forest are all common types of classification
algorithms

20.  Regression uses an algorithm to understand the relationship
between dependent and independent variables based on different
data points, such as sales revenue projections for a given business
 Some popular regression algorithms are

21. Unsupervis
ed Learning

22. Unsupervised learning uses machine learning algorithms to analyze
and cluster unlabeled data sets. The model can then find hidden
patternsorgroupsinthedatawithouthumanintervention.
UNLABELL
ED DATA MACHINE RESUL
TS

23. Types of
Unsupervised
Learning
Clustering
Association

24. Clustering is a data mining technique for grouping unlabeled data
based on their similarities or differences.
Independent Input
Variables
Classification
Model
Vegetables Groceries
Categorical Output
Variable

25. Association is another type of unsupervised learning method that uses different
rules to find relationships between variables in a given dataset.

26. Reinforcement
Learning

27. In this type, the model is trained by interacting with an
environment where it receives rewards or punishments for its
actions and then be used to make decisions in new situations to
maximize its rewards.

28. Semi-Supervised
Learning

29. Semi-supervised learning is partially supervised and
unsupervised.

30. Applications of
Machine
Learning

38. Advantages and
Disadvantages of
Machine
Learning

39. Easily identifies trends and pattern.
No human intervention needed. (Automation)
Continuous improvement.
Handling multi-dimensional and multi-variety data.
Wide Applications.

40. Data Acquisition
TIME AND RESOURCES
High error-susceptibility
Interpretation of Results
Algorithm selection