LAB MANUAL OF DATA VISUALIZATION AND MACHINE LEARNING
Submitted to
Dr. Raj Kumar
Quantum University, Roorkee
Towards the partial fulfilment of the degree of
BCA, 2nd Year
ACADEMIC SESSION : 2024-25
Candidate Faculty In charge
Name : Nitin Dr. Raj Kumar
Qid : 23120005
Section : B
Lesson: 1
What is Machine Learning?
(Layman’s term)
[ For understanding Deep Learning, first we need to know what is Machine Learning.
In this lesson, we will try to understand machine learning from a Layman’s term.]
Human can learn from past experience
and make decision of its own
3
What is this object?
4
What is this object?
CAR
CAR
BIKE
It is a CAR
BIKE
5
Let us ask the same question to him
What is this object?
6
Let us ask the same question to him
What is this object?
?
7
[ But, he is a human being. He can observe and
learn ]
Let us make him learn
show him
9
Let us make him learn
CAR
show him
CAR
BIKE
BIKE
10
Let us ask the same question now
What is this object?
CAR
CAR
BIKE
BIKE
Past experience 11
Let us ask the same question now
CAR What is this object?
CAR
CAR
BIKE
BIKE
12
What about a Machine ?
Machines follow instructions
[ It can not take decision of its own]
13
What about a Machine ?
We can ask a machine
• To perform an arithmetic operations such as
• Addition
• Multiplication
• Division
Machines follow instructions
14
What about a Machine ?
• Comparison
• Print
• Plotting a chart
Machines follow instructions
[ But, we can ask a machine to make a decision of its own ]
15
What is Machine Learning?
[ We want a machine to act like a human]
16
What is Machine Learning?
[ to identify this object.]
17
What is Machine Learning?
Price in 2025?
[ predict the price in future]
18
What is Machine Learning?
I made met him yesterday
[ Natural Language understand, and correct grammar ]
19
What is Machine Learning?
recognize face
[ Recognize Faces ]
20
What is Machine Learning?
[ What do we do?
Just like, what we did to human,
we need to provide experience
to the machine.
21
What is Machine Learning?
[
This what we called as Data
or Training dataset
+ So, we first need to provide
training dataset to the
machine
]
Dataset
22
What is Machine Learning?
+ +
[ Then, devise algorithms and execute programs on the
data
With respect to the underlying target tasks ]
Dataset
23
What is Machine Learning?
+ + +
Dataset [ Then, using the programs, Identify
required rules ]
24
What is Machine Learning?
+ + +
Dataset [extract required patterns ]
25
What is Machine Learning?
+ + +
Dataset
[ Identify relations ]
26
What is Machine Learning?
+ + + =
Dataset [ So that machine can derive inferences
from the data ]
27
In summary, what is machine learning?
Given a machine learning problem
• Identify and create the appropriate dataset
• Perform computation to learn
• Required rules, pattern and relations
• Output the decision
28
Machine Learning Paradigms
• Supervised
• Unsupervised Learning
• Reinforcement learning
[ We as human being solve various types of problem in our day-to-day life, <pause> Various decisions
need to be taken.
Depending on the nature of the problem, machine learning tasks can be broadly divided in ]
29
What is Supervised Learning?
CAR
CAR
+ BIKE
= Training Dataset
BIKE
Samples Labels
[In supervised learning, we need some thing called a Labelled Training Dataset ]
30
What is Supervised Learning?
CAR
CAR
+ BIKE
= Training Dataset 𝑓( , )=
BIKE
Samples Labels
[ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and
produces an output value.]
31
What is Supervised Learning?
CAR
CAR
+ BIKE
= Training Dataset 𝑓( , )=
BIKE
Samples Labels
[ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and
produces an output value.]
32
What is Supervised Learning?
CAR
CAR
+ BIKE
= Training Dataset 𝑓( , )= CAR
BIKE
Samples Labels
[ Given a labelled dataset, the task is to devise a function which takes the dataset, and a new sample, and
produces an output value.]
33
What is Supervised Learning?
CAR
Classification
CAR
+ BIKE
= Training Dataset 𝑓( , )= CAR
BIKE
Samples Labels
[ If the possible output values of the function are predefined and discrete/categorical, it is called
Classification
34
What is Supervised Learning?
CAR
Classification
CAR
+ BIKE
= Training Dataset 𝑓( , )= CAR
BIKE
Samples Labels
[ Predefined classes means, it will produce output only from the labels defined in the dataset. For example,
even if we input a bus, it will produce either CAR or BIKE ]
35
Classifier
Elephant
Elephant
Classifier
Tiger Identify the Animal ?
Dataset
36
Regression
Regression
𝑓( , )= 20500.50
Dataset
[ If the possible output values of the function are continuous real values, then it is called Regression
37
[
The classification and Regression problems are supervised, because the decision depends on the
characteristics of the ground truth labels or values present in the dataset, which we define as experience
]
38
What is Unsupervised Learning
CAR
CAR
BIKE
BIKE
Dataset
[ In the unsupervised learning, we do not need to know the labels or Ground truth values ]
39
What is Unsupervised Learning
Clustering
Dataset
[ The task is to identify the patterns like group the similar objects together ]
40
What is Unsupervised Learning
Association Rules Mining
Dataset
[ Association rules like ]
41
More Example Unsupervised Learning
Dataset
42
More Example Unsupervised Learning
Dataset
43
More Example Unsupervised Learning
44
What is Reinforcement Learning
[ It is also known as learning from trials and errors ]
45
What is Reinforcement Learning
46
What is Reinforcement Learning
47
What is Reinforcement Learning
48
Another Example
Agent Task Environment
49
Reinforcement Learning
Punishment
50
Reinforcement Learning
Reward
51
Reinforcement Learning
Reward
Baby Learn from the Trials and Errors
Reinforcement Learning 52
Summary
what is machine learning
what are the machine learning paradigms
[ In this lesion, we have learnt ]
