This document provides an overview of machine learning topics including linear regression, linear classification models, decision trees, random forests, supervised learning, unsupervised learning, reinforcement learning, and regression analysis. It defines machine learning, describes how machines learn through training, validation and application phases, and lists applications of machine learning such as risk assessment and fraud detection. It also explains key machine learning algorithms and techniques including linear regression, naive bayes, support vector machines, decision trees, gradient descent, least squares, multiple linear regression, bayesian linear regression, and types of machine learning models.

1. Unit 3 – Machine Learning
Introduction to machine learning – Linear Regression
Models: Least squares, single & multiple variables,
Bayesian linear regression, gradient descent, Linear
Classification Models: Discriminant function –
Probabilistic discriminative model - Logistic regression,
Probabilistic generative model – Naive Bayes, Maximum
margin classifier – Support vector machine, Decision
Tree, Random forests.

2. Machine Learning
• Learning is the process of gaining new
knowledge.
• Machine learning is a subfield of artificial
intelligence that involves the development of
algorithms and statistical models that enable
computers to improve their performance in
tasks through experience.

3. Definition of Machine learning
• A computer program is said to learn from
experience E with respect to some class of tasks T
and performance measure P, if its performance at
tasks T, as measured by P , improves with
experience E.
• The goal of machine learning is to build computer
systems that can adapt and learn from experience.

4. How machines learn ?
• Typically, machine learning follows three phases:
1. Training
2. Validation
3. Application

5. Applications of Machine learning

6. Types of Machine learning
• Supervised Learning
• Unsupervised Learning and
• Reinforcement Learning

7. Supervised learning
• Supervised learning is a type of machine learning in
which machines are trained using well "labelled"
training data, and on basis of that data, machines
predict the output.
• The labelled data means some input data is already
tagged with the correct output.
• In supervised learning, the training data provided to
the machines work as the supervisor that teaches the
machines to predict the output correctly.

8. • The aim of a supervised learning algorithm is to find a
mapping function to map the input variable(x) with
the output variable(y).
• In the real-world, supervised learning can be used
for Risk Assessment, Image classification, Fraud
Detection, spam filtering, etc.
How Supervised Learning Works?
• In supervised learning, models are trained using labelled
dataset, where the model learns about each type of data.
Once the training process is completed, the model is tested
on the basis of test data (a subset of the training set), and
then it predicts the output.

9. Training
data
Learning
algorithm
Model Test
data
Accur
acy
The working of Supervised learning can be easily understood by the below example

10. Steps Involved in Supervised Learning:
• First Determine the type of training dataset
• Collect/Gather the labelled training data.
• Split the training dataset into training dataset, test dataset &
validation dataset.
• Determine the input features of the training dataset, which
should have enough knowledge so that the model can
accurately predict the output.
• Determine the suitable algorithm for the model
• Execute the algorithm on the training dataset.
• Evaluate the accuracy of the model by providing the test set. If
the model predicts the correct output, which means our
model is accurate.

11. Unsupervised Learning
• Unsupervised learning is a type of machine learning in
which models are trained using unlabeled dataset and
are allowed to act on that data without any
supervision
• Unsupervised learning is helpful for finding useful
insights from the data.
• Unsupervised learning is much similar as a human
learns to think by their own experiences, which makes
it closer to the real AI.

12. • Unlabeled input data is given as input. First, it will
interpret the raw data to find the hidden patterns
from the data and then will apply suitable
algorithms, then processing the data to generate
output.

13. Differences between Supervised and Unsupervised learning
Supervised Learning Unsupervised Learning
Supervised learning algorithms are
trained using labeled data.
Unsupervised learning algorithms are
trained using unlabeled data.
Supervised learning model takes direct
feedback to check if it is predicting
correct output or not.
Unsupervised learning model does not
take any feedback.
Supervised learning model predicts the
output.
Unsupervised learning model finds the
hidden patterns in data.
In supervised learning, input data is
provided to the model along with the
output.
In unsupervised learning, only input data
is provided to the model.

14. Semi-Supervised Learning
• Semi-Supervised learning is a type of Machine
Learning algorithm that represents the
intermediate ground between Supervised and
Unsupervised learning algorithms.
• It uses the combination of labelled and
unlabeled datasets during the training period.

15. • The basic disadvantage of supervised learning is that
it requires hand-labelling by ML specialists or data
scientists, and it also requires a high cost to process.
• For unsupervised learning, it has a limited spectrum
for its applications.
• To overcome these drawbacks of supervised
learning and unsupervised learning algorithms, the
concept of Semi-supervised learning is introduced.

16. • Semi-supervised learning models are becoming
more popular in the industries.
• Some familiar applications are:
– Speech Analysis
– Web content classification
– Protein sequence classification
– Text document classifier

17. Reinforcement Learning
• User will get immediate feedback in Supervised
learning and no feedback from Unsupervised
learning.
• But in Reinforcement Learning, user will get
delayed scalar feedback.
• Reinforcement learning is a type of machine
learning method where an intelligent agent
(computer program) interacts with the
environment and learns to act within that

18. • For each good action,
the agent gets
positive feedback,
and for each bad
action, the agent gets
negative feedback or
penalty.

19. Elements of Reinforcement Learning
• There are four main elements of Reinforcement
Learning, which are given below:
1. Policy
2. Reward Signal
3. Value Function
4. Model of the environment

20. Regression
• Regression analysis is a statistical method to
model the relationship between a dependent
(target) and independent (predictor) variables
with one or more independent variables.
• It predicts continuous/real values such as house
prices, market trends, weather, Oil & gas prices.
• It is mainly used for prediction, forecasting, time
series modelling and determining the causal-
effect relationship between variables.

21. Types of Regressions
• Two basic types of regressions:
• Linear and Multiple linear
• Linear Regression:
• Linear regression is a statistical regression
method which is used for predictive analysis.
• It is one of the very simple and easy algorithms
which works on regression and shows the
relationship between the continuous variables.

22. • Linear regression shows the linear relationship
between the independent variable (X-axis) and the
dependent variable (Y-axis), hence called linear
regression.
• If there is only one input variable (x), then such
linear regression is called simple linear regression.
And if there is more than one input variable, then
such linear regression is called multiple linear
regression.
• The relationship between variables in the linear
regression model can be explained using the below
image.

23. • The regression line
gives the average
relationship
between two
variables X and Y.

24. • Regression line of X on Y: The best estimate for
the value of X for any specific values of Y is
X=a+bY
• where a = X intercept, b= Slope of the line,
X=dependent variable and Y=independent
variable.
• Regression line of Y on X: The best estimate
for the value of Y for any specific values of X is
Y=a+bX

25. Least Squares
• The least square method is the process of finding
the best-fitting line for a set of data points by
reducing the sum of the squares of the offsets of
the points from the line.
• It is a crucial statistical method that is practised
to find a regression line or a best-fit line for the
given pattern.
• This method is described by an equation with
specific parameters

26. The given data points are to
be minimized by the method
of reducing residuals or
offsets of each point from
the line. The vertical offsets
are generally used in surface,
polynomial and hyperplane
problems, while
perpendicular offsets are
utilized in common practice

27. Multiple Linear Regression
• Multiple Linear Regression is one of the
important regression algorithms which models
the linear relationship between a single
dependent continuous variable and more than
one independent variable.
• In Multiple Linear Regression, the target
variable(Y) is a linear combination of multiple
predictor variables x1, x2, x3, ...,xk.

28. • Let k represent the number of variables denoted
by x1, x2, x3, ……, xk.
• For this method, we assume that we have k
independent variables x1, . . . , xk that we can
set, then they probabilistically determine an
outcome Y.
• Furthermore, we assume that Y is linearly
dependent on the factors according to
• Y = β0 + β1x1 + β2x2 + · · · + βkxk + ε

29. Difference Between Linear and Multiple
Regression
• A simple linear regression can accurately capture
the relationship between two variables in simple
relationships.
• On the other hand, multiple linear regression can
capture more complex interactions that require
more thought.
• When predicting a complex process's outcome, it
is best to use multiple linear regression instead of
simple linear regression.

30. Bayesian Linear Regression
• Bayesian Regression can be very useful when
we have insufficient data in the dataset or the
data is poorly distributed.
• The output of a Bayesian Regression model is
obtained from a probability distribution, as
compared to regular regression techniques
where the output is just obtained from a single
value of each attribute

31. • The aim of Bayesian Linear Regression is not
to find the model parameters, but rather to
find the ‘posterior‘ distribution for the model
parameters.
• Not just the output y, but the model
parameters are also assumed to come from a
distribution.

32. Advantages of Bayesian Regression
• Very effective when the size of the dataset is
small.
• Particularly well-suited for on-line based
learning
• Bayesian approach is a tried and tested
approach and is very robust, mathematically.

33. Disadvantages of Bayesian Regression
• The inference of the model can be time-
consuming.
• If there is a large amount of data available for
our dataset, the Bayesian approach is not
worth

34. Gradient Descent
• Gradient Descent is one of the most commonly
used optimization algorithms to train machine
learning models by means of minimizing errors
between actual and expected results.
• Further, gradient descent is also used to train
Neural Networks.
• The main objective of using a gradient descent
algorithm is to minimize the cost function using
iteration

35. How does Gradient Descent work ?
• Before starting the working principle of gradient
descent, we should know some basic concepts
to find out the slope of a line from linear
regression.
• The equation for simple linear regression is
given as: Y=mX+c
• where 'm' represents the slope of the line, and
'c' represents the intercepts on the y-axis

36. • The starting point( as shown in
fig.) is used to evaluate the
performance as it is considered
just as an arbitrary point.
• At this starting point, we will
derive the first derivative or
slope and then use a tangent line
to calculate the steepness of this
slope.
• Further, this slope will inform the
updates to the parameters
(weights and bias)

37. Steepest Descent
• The best way to define the local minimum or local
maximum of a function using gradient descent is as
follows:
• If we move towards a negative gradient or away from the
gradient of the function at the current point, it will give
the local minimum of that function.
• Whenever we move towards a positive gradient or
towards the gradient of the function at the current point,
we will get the local maximum of that function.

38. • This entire procedure is
known as Gradient
Ascent, which is also
known as steepest
descent.
• To achieve this goal, it
performs two steps
iteratively:

39. • Calculates the first-order derivative of the
function to compute the gradient or slope of
that function.
• Move away from the direction of the gradient,
which means slope increased from the current
point by alpha times, where Alpha is defined as
Learning Rate.

40. • Learning Rate: It is defined as the step size taken to
reach the minimum or lowest point.
• This is typically a small value that is evaluated and
updated based on the behaviour of the cost
function.
• If the learning rate is high, it results in larger steps.
• If learning rate is low, it shows the small step sizes,
which compromises overall efficiency but gives the
advantage of more precision.

42. Linear Classification Model
• A Classification algorithm makes its classification
based on a linear predictor function combining a
set of weights with the feature vector.
• Linear Discriminant analysis:
Linear Discriminant analysis is one of the most
popular dimensionality reduction techniques
used for supervised classification problems in
machine learning.

43. • Whenever there is a requirement to separate
two or more classes having multiple features
efficiently, the LDA model is considered the
most common technique to solve such
classification problems.
• For e.g., if we have two classes with multiple
features and need to separate them efficiently.
When we classify them using a single feature,
then it may show overlapping.

45. • LDA algorithm works based on 6 steps as follows:
Step 1 - First calculate means and std deviation of each
feature.
Step 2 - Class scalar matrix is calculated.
Step 3 - LDA chooses k eigenvectors and their corresponding
eigenvalues for the scatter matrices.
Step 4 - Creating a new matrix that will contain the
eigenvectors mapped to the k eigenvalues.
Step 5 - Obtaining new features by taking the dot product of
the data and the matrix from Step 4.
Step 6 – LDA can be used for classification or dimensionality
reduction.

46. Benefits of LDA
• LDA is used for classification problems
• LDA is used for dimensionality reduction

47. Logistic regression
• Logistic regression is one of the most popular
Machine Learning algorithms, which comes
under the Supervised Learning technique.
• It is used for predicting the categorical
dependent variable using a given set of
independent variables.

48. • Logistic regression predicts the output of a
categorical dependent variable.
• Therefore the outcome must be a categorical or
discrete value.
• It can be either Yes or No, 0 or 1, true or False,
etc. but instead of giving the exact value as 0
and 1, it gives the probabilistic values which lie
between 0 and 1.

49. Types of Logistic Regression
1. Binary Logistic Regression
• The categorical response has only two 2 possible
outcomes. Example: True or False
2. Multinomial Logistic Regression
• Three or more categories without ordering.
Example: Predicting which food is preferred more
(Indian, Chinese, Contenential)
3. Ordinal Logistic Regression
• Three or more categories with ordering. Example:
Movie rating from 1 to 5

50. • In Logistic regression, instead of fitting a
regression line, we fit an "S" shaped logistic
function, which predicts two maximum values
(0 or 1).
• The curve from the logistic function indicates
the likelihood of something.
• The below image is showing the logistic
function:

52. • Logistic Regression equation of the straight
line can be written as :
• Logistic Regression y can be between 0 and 1

53. Naive Bayes
• Naive Bayes algorithm is a supervised learning
algorithm, which is based on Bayes theorem and
used for solving classification problems.
• It is mainly used in text classification that includes a
high-dimensional training dataset.
• Naïve Bayes Classifier is one of the simple and most
effective Classification algorithms which helps in
building the fast machine learning models that can
make quick predictions.

54. • Some popular examples of Naïve Bayes
Algorithm are spam filtration, Sentimental
analysis, and classifying articles.
• Conditional Probability
• Conditional probability is possibility of an event
or outcome happening, based on the existence
of a previous event or outcome.

55. • Suppose A and B be the two independent
events with probabilities respectively P(A) and
P(B) such that the probability of occurrence of
event B given A is given by:
P(B|A) = P(A ∩ B)/P(A)
• Similarly, the probability of occurrence of
event A given B is given by:
P(A|B) = P(A ∩ B)/P(B)

56. Joint Probability
• Joint probability is a statistical measure that
calculates the likelihood of two events occurring
together and at the same point in time.
• Formula for Joint Probability
• The following formula represents the joint
probability of events with intersection.
• P (A⋂B) where, A, B= Two events
• P(A and B)=P(A)P(B)

57. Bayes' Theorem
• Bayes' Theorem states that the conditional
probability of an event, based on the occurrence
of another event, is equal to the likelihood of the
second event given the first event multiplied by
the probability of the first event.
• Bayes' Theorem gives relation between P(A|B)
and P(B|A).

58. • The above equation is called as Bayes' rule or Bayes'
theorem.
• This equation is basic of most modern AI systems
for probabilistic inference.
• P(A|B) is known as posterior, which we need to
calculate, and it will be read as Probability of
hypothesis A when we have occurred an evidence B.

59. • P(A) is called the prior probability, probability
of hypothesis before considering the evidence
• P(B) is called marginal probability, pure
probability of an evidence.
• In the equation (a), in general, we can write P
(B) = P(A)*P(B|Ai), hence the Bayes' rule can
be written as:

60. Support Vector Machine(SVM)
• Support Vector Machine(SVM) is algorithm used for both
classification and regression.
• The objective of the SVM algorithm is to find a
hyperplane in an N-dimensional space that distinctly
classifies the data points.
• The dimension of the hyperplane depends upon the
number of features.
• If the number of input features is two, then the
hyperplane is just a line. If the number of input features is
three, then the hyperplane becomes a 2-D plane

61. • The goal of the SVM algorithm is to create the
best line or decision boundary that can
segregate n-dimensional space into classes so
that we can easily put the new data point in
the correct category in the future.
• This best decision boundary is called a
hyperplane.

62. • Bad Decision boundary