Machine Learning Overview.pptx

Machine Learning Overview
Rushikesh Chikane, MIT ACSC, Alandi
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Chapter 2

Introduction to Machine Learning
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 Both machine learning and data science are like buzzwords in today’s technical
world.
 Though data science includes machine learning as one of its fundamental areas of
study, machine learning in itself is a vast research area of study that requires good
skills and experience to expertise.
 The basic idea of machine learning is to allow machines (computers) to
independently learn from the wealth of data that is fed as input into the machine.
 Machine Learning is said as a subset of artificial intelligence that is mainly
concerned with the development of algorithms which allow a computer to learn from
the data and past experiences on their own.
 Machine learning enables a machine to automatically learn from data, improve
performance from experiences, and predict things without being explicitly
programmed.
 They started observing how human beings learn from their interaction with nature.
 They could observe that humans either learned through the hit and trial method or in
the presence of some supervisors.

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 With the advancement of new technology, machines are being trained to
behave like a human in decision-making capability.
 In doing so, it is necessary to automate decisions that can be inferred by the
machines with the interaction with the environment and understanding from
past knowledge.
 The accuracy of predicted output depends upon the amount of data, as the
huge amount of data helps to build a better model which predicts the output
more accurately.
 The field of machine learning deals with all those algorithms that help
machines to get self-trained in this process.
 Following are some key points which show the importance of
Machine Learning:
 Rapid increment in the production of data
 Solving complex problems, which are difficult for a human
 Decision making in various sector including finance
 Finding hidden patterns and extracting useful information from data.

Advantages of ML
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1. Easily identifies trends and patterns
• Machine Learning can review large volumes of data and discover
specific trends and patterns that would not be apparent to humans.
2. No human intervention needed (automation)
• With ML, you don’t need to babysit your project every step of the
way. Since it means giving machines the ability to learn, it lets
them make predictions and also improve the algorithms on their
own.
3. Continuous Improvement
• As ML algorithms gain experience, they keep improving in
accuracy and efficiency. This lets them make better decisions.

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4. Handling multi-dimensional and multi-variety data
• Machine Learning algorithms are good at handling data that are multi-
dimensional and multi-variety, and they can do this in dynamic or
uncertain environments.
5. Wide Applications
• You could be an e-tailer or a healthcare provider and make ML work for
you. Where it does apply, it holds the capability to help deliver a much
more personal experience to customers while also targeting the right
customers.

Disadvantages of ML
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• Machine Learning requires massive data sets to train
on, and these should be inclusive/unbiased, and of
good quality. There can also be times where they must
wait for new data to be generated.
1. Data Acquisition
• ML needs enough time to let the algorithms learn and
develop enough to fulfill their purpose with a
considerable amount of accuracy and relevancy. It also
needs massive resources to function. This can mean
additional requirements of computer power for you.
2. Time and Resources

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• Another major challenge is the ability to accurately
interpret results generated by the algorithms. You
must also carefully choose the algorithms for your
purpose.
3. Interpretation of Results
• Machine Learning is autonomous but highly
susceptible to errors. Suppose you train an algorithm
with data sets small enough to not be inclusive. You
end up with biased predictions coming from a biased
training set. This leads to irrelevant advertisements
being displayed to customers.
4. High error-susceptibility

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Types of machine learning
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Types of
machine learning
Supervised
machine learning
Unsupervised
machine learning
Semi-supervised
learning
Reinforcement
learning

Supervised machine learning
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 Supervised learning is the types 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.
 Supervised learning is a process of providing input data as
well as correct output data to the machine learning model.
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

How Supervised Learning Works?
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 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.
 The working of Supervised learning can be easily
understood by the below example and diagram:

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 Suppose we have a dataset of different types of
shapes which includes square, rectangle, triangle, and
Polygon. Now the first step is that we need to train the
model for each shape.
 If the given shape has four sides, and all the sides are
equal, then it will be labelled as a Square.
 If the given shape has three sides, then it will be
labelled as a triangle.
 If the given shape has six equal sides then it will be
labelled as hexagon.
 Now, after training, we test our model using the test
set, and the task of the model is to identify the shape.
 The machine is already trained on all types of shapes,
and when it finds a new shape, it classifies the shape
on the bases of a number of sides, and predicts the
output.

Steps Involved in Supervised
Learning:
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First Determine the type of training dataset
Collect/Gather the labelled training data.
Split the training dataset into training dataset, test
dataset, and validation dataset.
Determine the input features of the training dataset,
which should have enough knowledge so that the
model can accurately predict the output.

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Determine the suitable algorithm for the
model, such as support vector machine,
decision tree, etc.
Execute the algorithm on the training
dataset. Sometimes we need validation
sets as the control parameters, which
are the subset of training datasets.
Evaluate the accuracy of the model by
providing the test set. If the model
predicts the correct output, which
means our model is accurate.

Types of supervised Machine
learning Algorithms:
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1. Regression
Regression algorithms are used if there is a relationship
between the input variable and the output variable. It
is used for the prediction of continuous variables, such as
Weather forecasting, Market Trends, etc. Below are some
popular Regression algorithms which come under
supervised learning:
Linear
Regression
Polynomial
Regression
Logistic
Regression

Linear Regression
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 Linear regression maps an independent variable to a
dependent variable by a linear equation. Many times an
independent variable can have a deterministic mapping to a
dependent variable.
 It is a statistical method that is used for predictive analysis.
 Linear regression makes predictions for continuous/real or
numeric variables such as sales, salary, age, product
price, etc.
 Linear regression algorithm shows a linear relationship
between a dependent (y) and one or more independent (X)
variables, hence called as linear regression.
 Since linear regression shows the linear relationship, which
means it finds how the value of the dependent variable is
changing according to the value of the independent

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A very plain form of mapping an independent variable to a
dependent variable is a linear equation:
y= a0+a1x+ ε
 Here,
 Y= Dependent Variable (Target Variable)
X= Independent Variable (predictor Variable)
a0= intercept of the line (Gives an additional degree of
freedom)
a1 = Linear regression coefficient (scale factor to each
input value).
ε = random error
 Performing linear regression would enable to find the best
fit line, that is, the line that best separates two classes with
minimum error
 The linear regression model provides a sloped straight line

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 For Linear Regression, we use the Mean
Squared Error (MSE) cost function, which is the
average of squared error occurred between the
predicted values and actual values. It can be
written as:
 Where,
 N=Total number of observation
Yi = Actual value
(a1xi+a0)= Predicted value

Polynomial regression
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 If your data points clearly will not fit a linear
regression (a straight line through all data points), it
might be ideal for polynomial regression.
 Polynomial regression, like linear regression, uses
the relationship between the variables x and y to find
the best way to draw a line through the data points.
 Polynomial Regression is a regression algorithm that
models the relationship between a dependent(y) and
independent variable(x) as nth degree polynomial.
The Polynomial Regression equation is given below:
 y= b0+b1x1+ b2x1
2+ b2x1
3+...... bnx1
n

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 It is also called the special case of Multiple Linear
Regression in ML. Because we add some polynomial
terms to the Multiple Linear regression equation to
convert it into Polynomial Regression.
 It is a linear model with some modification in order to
increase the accuracy.
 The dataset used in Polynomial regression for training
is of non-linear nature.
 It makes use of a linear regression model to fit the
complicated and non-linear functions and datasets.
 Hence, "In Polynomial regression, the original
features are converted into Polynomial features of
required degree (2,3,..,n) and then modeled using
a linear model."

Need for Polynomial Regression:
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 If we apply a linear model on a linear dataset,
then it provides us a good result as we have seen
in Simple Linear Regression, but if we apply the
same model without any modification on a non-
linear dataset, then it will produce a drastic
output. Due to which loss function will increase,
the error rate will be high, and accuracy will be
decreased.
 So for such cases, where data points are
arranged in a non-linear fashion, we need the
Polynomial Regression model. We can
understand it in a better way using the below
comparison diagram of the linear dataset and

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 2. Classification
 Classification algorithms are used when the output
variable is categorical, which means there are two
classes such as Yes-No, Male-Female, True-false,
etc.
 Spam Filtering,
 Random Forest
 Decision Trees
 Logistic Regression
 Support vector Machines

Logistic Regression
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 It is used for predicting the categorical dependent variable
using a given set of independent variables.
 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.
 Logistic Regression is much similar to the Linear Regression
except that how they are used. Linear Regression is used for
solving Regression problems, whereas Logistic regression
is used for solving the classification problems.
 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 such as whether the cells are cancerous or not, a
mouse is obese or not based on its weight, etc.

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Logistic Regression Equation:
We know the equation of the straight line can be written as:
we need range between -[infinity] to +[infinity], then take logarithm of the
equation it will become:

Type of Logistic Regression:
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Binomial:
• In binomial
Logistic
regression,
there can be
only two
possible types
of the
dependent
variables, such
as 0 or 1, Pass
or Fail, etc.
Multinomial:
• In multinomial
Logistic
regression,
there can be 3
or more possible
unordered types
of the
dependent
variable, such
as "cat", "dogs",
or "sheep"
Ordinal:
• In ordinal
Logistic
regression,
there can be 3
or more possible
ordered types of
dependent
variables, such
as "low",
"Medium", or
"High".

Supervised learning examples
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 Image- and object-recognition: Supervised learning
algorithms can be used to locate, isolate, and
categorize objects out of videos or images, making
them useful when applied to various computer vision
techniques and imagery analysis.
 Predictive analytics: A widespread use case for
supervised learning models is in creating predictive
analytics systems to provide deep insights into
various business data points. This allows enterprises
to anticipate certain results based on a given output
variable, helping business leaders justify decisions or
pivot for the benefit of the organization.

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 Customer sentiment analysis: Using supervised
machine learning algorithms, organizations can
extract and classify important pieces of
information from large volumes of data—including
context, emotion, and intent—with very little
human intervention. This can be incredibly useful
when gaining a better understanding of customer
interactions and can be used to improve brand
engagement efforts.
 Spam detection: Spam detection is another
example of a supervised learning model. Using
supervised classification algorithms,
organizations can train databases to recognize
patterns or anomalies in new data to organize
spam and non-spam-related correspondences

Advantages of Supervised learning:
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With the help of supervised learning, the
model can predict the output on the basis
of prior experiences
In supervised learning, we can have an
exact idea about the classes of objects.
Supervised learning model helps us to
solve various real-world problems such
as fraud detection, spam filtering, etc.

Disadvantages of supervised
learning:
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
Supervised learning models are not suitable for
handling the complex tasks.
Supervised learning cannot predict the correct output if
the test data is different from the training dataset
Training required lots of computation times.
In supervised learning, we need enough knowledge
about the classes of object.

Unsupervised machine learning
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 In unsupervised learning, there is just raw data,
without any particular thing that is supposed to be
predicted.
 Unsupervised algorithms are used for finding
patterns in the data in general, teasing apart its
underlying structure.
 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.

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 Clustering algorithms, which try to break a dataset
down into “natural” clusters, are a prototypical example
of unsupervised learning.
 Unsupervised learning cannot be directly applied to a
regression or classification problem because unlike
supervised learning, we have the input data but no
corresponding output data. The goal of unsupervised
learning is to find the underlying structure of
dataset, group that data according to similarities,
and represent that dataset in a compressed format.

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 Supervised learning is somewhat more common in real
applications.
 Business situations usually dictate a specific thing that
you are trying to predict, rather than a broad “see what
there is to see” approach.
 However, unsupervised learning algorithms are often
used as a preprocessing step for extracting meaningful
features from a data point, with those features
ultimately getting used for supervised learning.

Why use Unsupervised
Learning?
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 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.
 Unsupervised learning works on unlabeled and
uncategorized data which make unsupervised
learning more important.
 In real-world, we do not always have input data
with the corresponding output so to solve such
cases, we need unsupervised learning.

Working of Unsupervised Learning
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 Here, we have taken an unlabeled input data,
which means it is not categorized and
corresponding outputs are also not given. Now,
this unlabeled input data is fed to the machine
learning model in order to train it. Firstly, it will
interpret the raw data to find the hidden patterns
from the data and then will apply suitable
algorithms such as k-means clustering, Decision
tree, etc.
 Once it applies the suitable algorithm, the
algorithm divides the data objects into groups
according to the similarities and difference
between the objects.

Types of Unsupervised Learning
Algorithm:
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 Clustering: Clustering is a method of grouping the
objects into clusters such that objects with most
similarities remains into a group and has less or no
similarities with the objects of another group. Cluster
analysis finds the commonalities between the data
objects and categorizes them as per the presence and
absence of those commonalities.
 Association: An association rule is an unsupervised
learning method which is used for finding the
relationships between variables in the large database.
It determines the set of items that occurs together in
the dataset. Association rule makes marketing strategy
more effective. Such as people who buy X item
(suppose a bread) are also tend to purchase Y
(Butter/Jam) item. A typical example of Association rule
is Market Basket Analysis.

Unsupervised Learning algorithms:
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 K-means clustering
 KNN (k-nearest neighbors)
 Hierarchal clustering
 Anomaly detection
 Neural Networks
 Principle Component Analysis
 Independent Component Analysis
 Apriori algorithm
 Singular value decomposition

Applications of unsupervised
learning
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 News Sections: Google News uses unsupervised learning
to categorize articles on the same story from various online
news outlets. For example, the results of a presidential
election could be categorized under their label for “US”
news.
 Computer vision: Unsupervised learning algorithms are
used for visual perception tasks, such as object recognition.
 Medical imaging: Unsupervised machine learning provides
essential features to medical imaging devices, such as
image detection, classification and segmentation, used in
radiology and pathology to diagnose patients quickly and
accurately.
 Anomaly detection: Unsupervised learning models can
comb through large amounts of data and discover atypical
data points within a dataset. These anomalies can raise
awareness around faulty equipment, human error, or

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 Customer personas: Defining customer
personas makes it easier to understand common
traits and business clients' purchasing habits.
Unsupervised learning allows businesses to build
better buyer persona profiles, enabling
organizations to align their product messaging
more appropriately.
 Recommendation Engines: Using past
purchase behavior data, unsupervised learning
can help to discover data trends that can be used
to develop more effective cross-selling strategies.
This is used to make relevant add-on
recommendations to customers during the

Advantages of Unsupervised
Learning
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 Unsupervised learning is used for more complex
tasks as compared to supervised learning
because, in unsupervised learning, we don't have
labeled input data.
 Unsupervised learning is preferable as it is easy
to get unlabeled data in comparison to labeled
data.

Disadvantages of Unsupervised Learning
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 More difficult than supervised learning as it does not
have corresponding output.
 The result of the unsupervised learning algorithm might
be less accurate as input data is not labeled
 Algorithms do not know the exact output in advance.
 Computational complexity due to a high volume of
training data
 Longer training times
 Higher risk of inaccurate results
 Human intervention to validate output variables
 Lack of transparency into the basis on which data was
clustered

Semi Supervised Learning
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 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 labeled and unlabeled datasets
during the training period.
 Although Semi-supervised learning is the middle
ground between supervised and unsupervised
learning and operates on the data that consists of a
few labels, it mostly consists of unlabeled data. As
labels are costly, but for the corporate purpose, it may
have few labels.
 semi-supervised learning (SSL) is a machine
learning technique that uses a small portion of labeled
data and lots of unlabeled data to train a predictive
model.

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 Semi-supervised learning bridges supervised
learning and unsupervised learning techniques to
solve their key challenges. With it, you train an
initial model on a few labeled samples and then
iteratively apply it to the greater number of
unlabeled data.
 Unlike unsupervised learning, SSL works for a
variety of problems from classification and
regression to clustering and association.
 Unlike supervised learning, the method uses
small amounts of labeled data and also large
amounts of unlabeled data, which reduces
expenses on manual annotation and cuts data
preparation time.

Working of Semi-Supervised
Learning
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Firstly, it trains the model with less amount of training
data similar to the supervised learning models. The
training continues until the model gives accurate results.
The algorithms use the unlabeled dataset with pseudo
labels in the next step, and now the result may not be
accurate.
Now, the labels from labeled training data and pseudo
labels data are linked together.
The input data in labeled training data and unlabeled
training data are also linked.
In the end, again train the model with the new combined
input as did in the first step. It will reduce errors and
improve the accuracy of the model.

Real-world applications of Semi-
supervised Learning-
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• It is the most classic example of semi-supervised learning
applications. Since, labeling the audio data is the most impassable
task that requires many human resources, this problem can be
naturally overcome with the help of applying SSL in a Semi-
supervised learning model.
Speech
Analysis-
•However, this is very critical and impossible to label each page on the internet
because it needs mode human intervention. Still, this problem can be reduced
through Semi-Supervised learning algorithms.
Further, Google also uses semi-supervised learning algorithms to rank a
webpage for a given query.
Web content
classification-
• Protein sequence classification
Protein
sequence
classification
• As we know, it would be very unfeasible to find a large amount of
labeled text data, so semi-supervised learning is an ideal model to
overcome this.
Text document
classifier

Reinforcement Learning
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 Reinforcement learning uses algorithms that learn
from outcomes and decide which action to take
next. After each action, the algorithm receives
feedback that helps it determine whether the
choice it made was correct, neutral or incorrect. It
is a good technique to use for automated systems
that have to make a lot of small decisions without
human guidance.
 Reinforcement learning is an autonomous, self-
teaching system that essentially learns by trial
and error. It performs actions with the aim of
maximizing rewards, or in other words, it is
learning by doing in order to achieve the best
outcomes.

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 Reinforcement Learning is a feedback-based Machine
learning technique in which an agent learns to behave
in an environment by performing the actions and
seeing the results of actions. For each good action,
the agent gets positive feedback, and for each bad
action, the agent gets negative feedback or penalty.
 In Reinforcement Learning, the agent learns
automatically using feedbacks without any labeled
data, unlike supervised learning.
 Since there is no labeled data, so the agent is bound
to learn by its experience only.
 RL solves a specific type of problem where decision
making is sequential, and the goal is long-term, such
as game-playing, robotics, etc.

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 The agent learns with the process of hit and trial,
and based on the experience, it learns to perform
the task in a better way. Hence, we can say
that "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.

Reinforcement Learning Applications
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Advantages of Reinforcement
learning
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 1. Reinforcement learning can be used to solve
very complex problems that cannot be solved by
conventional techniques.
 2. The model can correct the errors that occurred
during the training process.
 3. In RL, training data is obtained via the direct
interaction of the agent with the environment

Disadvantages of Reinforcement
learning
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 1. Reinforcement learning is not preferable to use
for solving simple problems.
 2. Reinforcement learning needs a lot of data and
a lot of computation

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Deep Learning
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 Deep learning is often used in data science as it is computationally very
competent compared to traditional machine learning methods, which
 require human intervention before being machine trained. The big players in the
market such as Google, Microsoft, and Amazon need to deal with
 large volumes of data on a daily basis for business analysis and effective
decision-making. Deep learning helps in analyzing a bulk amount of data
 through a hierarchical learning process. The amount of data generated in these
companies is massive, raw and unstructured for which deep
 learning approaches are used to generate meaningful results.
 Deep learning approaches have proven to outperform all other machine learning
techniques especially in the field of image and speech
 recognition systems. A deep learning network obliges representation learning
incorporating multiple levels of representation. In a simple sense, it
 could be understood as such that the higher levels of the network amplify input
aspects that are relevant to classification ignoring the irrelevant
 features that are not significant to the classification process. The interesting fact
to note is that these layers of features in the deep network are
 not designed by human engineers but are learned from data using general-
purpose learning procedures

Advantages of Deep Learning
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 Best in-class performance on problems.
 Reduces need for feature engineering.
 Eliminates unnecessary costs.
 Identifies defects easily that are difficult to detect.
Disadvantages of Deep Learning
 Large amount of data required.
 Computationally expensive to train.
 No strong theoretical foundation.

Applications :
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 Automatic Text Generation – Corpus of text is learned and
from this model new text is generated, word-by-word or
character-by-character. Then this model is capable of learning
how to spell, punctuate, form sentences, or it may even capture
the style.
 Healthcare – Helps in diagnosing various diseases and treating
it.
 Automatic Machine Translation – Certain words, sentences or
phrases in one language is transformed into another language
(Deep Learning is achieving top results in the areas of text,
images).
 Image Recognition – Recognizes and identifies peoples and
objects in images as well as to understand content and context.
This area is already being used in Gaming, Retail, Tourism, etc.
 Predicting Earthquakes – Teaches a computer to perform
viscoelastic computations which are used in predicting
earthquakes.
 Deep learning has a wide range of applications in various fields
such as computer vision, speech recognition, natural language
processing, Robotics, Finance Gaming, Social Media etc.

Artificial Intelligence
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 Artificial intelligence is a field of computer science
which makes a computer system that can mimic
human intelligence. It is comprised of two words
"Artificial" and "intelligence", which means "a
human-made thinking power." Hence we can
define it as,
 Artificial intelligence is a technology using which
we can create intelligent systems that can
simulate human intelligence.The Artificial
intelligence system does not require to be pre-
programmed, instead of that, they use such
algorithms which can work with their own
intelligence. It involves machine learning
algorithms such as Reinforcement learning
algorithm and deep learning neural networks

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 The Artificial intelligence system does not require
to be pre-programmed, instead of that, they use
such algorithms which can work with their own
intelligence.
 It involves machine learning algorithms such as
Reinforcement learning algorithm and deep
learning neural networks. AI is being used in
multiple places such as Siri, Google’s AlphaGo, AI
in Chess playing, etc.

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 On a broad level, we can differentiate both AI and
ML as
 AI is a bigger concept to create intelligent
machines that can simulate human thinking
capability and behavior, whereas, machine
learning is an application or subset of AI that
allows machines to learn from data without being
programmed explicitly.

Key differences between Artificial Intelligence
(AI) and Machine learning (ML)
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Artificial Intelligence Machine learning
Artificial intelligence is a technology
which enables a machine to simulate
human behavior.
Machine learning is a subset of AI which
allows a machine to automatically learn
from past data without programming
explicitly.
The goal of AI is to make a smart
computer system like humans to solve
complex problems.
The goal of ML is to allow machines to
learn from data so that they can give
accurate output.
In AI, we make intelligent systems to
perform any task like a human.
In ML, we teach machines with data to
perform a particular task and give an
accurate result.
Machine learning and deep learning are
the two main subsets of AI.
Deep learning is a main subset of
machine learning.
AI has a very wide range of scope. Machine learning has a limited scope.
AI is working to create an intelligent
system which can perform various
Machine learning is working to create
machines that can perform only those

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Artificial Intelligence Machine learning
AI system is concerned about
maximizing the chances of success.
Machine learning is mainly concerned
about accuracy and patterns.
The main applications of AI are Siri,
customer support using catboats,
Expert System, Online game playing,
intelligent humanoid robot, etc.
The main applications of machine
learning are Online recommender
system, Google search
algorithms, Facebook auto friend
tagging suggestions, etc.
On the basis of capabilities, AI can be
divided into three types, which
are, Weak AI, General AI, and Strong
AI.
Machine learning can also be divided
into mainly three types that
are Supervised learning, Unsupervised
learning, and Reinforcement learning.
It includes learning, reasoning, and self-
correction.
It includes learning and self-correction
when introduced with new data.
AI completely deals with Structured,
semi-structured, and unstructured data.
Machine learning deals with Structured
and semi-structured data.

Applications for machine learning in data
science
69

References
70
 Data Science Fundamentals and Practical
Approaches Understand Why Data Science Is the
Next (Dr. Gypsy Nandi, Dr. Rupam Kumar
Sharma) (z-lib.org)
 Data Mining Concepts and Techniques, Jiawei
Han, Micheline Kamber, Jian Pei, Morgan
Kaufmann, Third Edition, 2012.
 The-Morgan-Kaufmann-Series-in-Data-
Management-Systems-Jiawei-Han-Micheline-
Kamber-Jian-Pei-Data-Mining.-Concepts-and-
Techniques-3rd-Edition-Morgan-Kaufmann-2011

71
Thank
you

Machine Learning Overview.pptx

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