MACHINE LEARNING LECTURE NOTES
MACHINE LEARNING
UNIT – I
Introduction: Introduction to Machine learning, Supervised learning, Unsupervised learning,
Reinforcement learning. Deep learning.
Feature Selection: Filter, Wrapper , Embedded methods.
Feature Normalization :-min-max normalization, z-score normalization, and constant factor
normalization
Introduction to Dimensionality Reduction : Principal Component Analysis(PCA), Linear
Discriminant Analysis(LDA)
What is Machine Learning?
Machine Learning is a concept which allows the machine to learn from examples and experience,
and that too without being explicitly programmed. So instead of you writing the code, what you do
is you feed data to the generic algorithm, and the algorithm/ machine builds the logic based on the
given data.
How does Machine Learning Work?
Machine Learning algorithm is trained using a training data set to create a model. When new input
data is introduced to the ML algorithm, it makes a prediction on the basis of the model.The
prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning
algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained
again and again with an augmented training data set
BY
B SARITHA 1
,MACHINE LEARNING LECTURE NOTES
Types of Machine Learning
Machine learning is sub-categorized to three types:
Supervised Learning – Train Me!
Unsupervised Learning – I am self sufficient in learning
Reinforcement Learning – My life My rules! (Hit & Trial)
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We
have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model
gets trained it can start making a prediction or decision when new data is given to it.
What is Unsupervised Learning?
The model learns through observation and finds structures in the data. Once the model is given a
dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What
it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will
separate all the apples from mangoes.
Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based
on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now
if a new data is fed to the model, it adds it to one of the created clusters.
What is Reinforcement Learning?
It is the ability of an agent to interact with the environment and find out what is the best outcome. It
follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a
correct or a wrong answer, and on the basis of the positive reward points gained the model trains
itself. And again once trained it gets ready to predict the new data presented to it.
BY
B SARITHA 2
,MACHINE LEARNING LECTURE NOTES
Deep Learning:
Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure
and function of the brain. Deep learning is a subset of machine learning, which is a part of artificial
intelligence (AI).
Deep Learning is a subset of Machine Learning that is based on artificial neural networks (ANNs)
with multiple layers, also known as deep neural networks (DNNs). These neural networks are inspired
by the structure and function of the human brain, and they are designed to learn from large amounts
of data in an unsupervised or semi-supervised manner.
Deep Learning models are able to automatically learn features from the data, which makes them well-
suited for tasks such as image recognition, speech recognition, and natural language processing. The
most widely used architectures in deep learning are feedforward neural networks, convolutional
neural networks (CNNs), and recurrent neural networks (RNNs).
BY
B SARITHA 3
, MACHINE LEARNING LECTURE NOTES
Feedforward neural networks (FNNs) are the simplest type of ANN, with a linear flow of information
through the network. FNNs have been widely used for tasks such as image classification, speech
recognition, and natural language processing.
Convolutional Neural Networks (CNNs) are a special type of FNNs designed specifically for image
and video recognition tasks. CNNs are able to automatically learn features from the images, which
makes them well-suited for tasks such as image classification, object detection, and image
segmentation.
Recurrent Neural Networks (RNNs) are a type of neural networks that are able to process sequential
data, such as time series and natural language. RNNs are able to maintain an internal state that
captures information about the previous inputs, which makes them well-suited for tasks such as
speech recognition, natural language processing, and language translation.
Deep Learning models are trained using large amounts of labeled data and require significant
computational resources. With the increasing availability of large amounts of data and computational
resources, deep learning has been able to achieve state-of-the-art performance in a wide range of
applications such as image and speech recognition, natural language processing, and more.
Deep learning is a particular kind of machine learning that achieves great power and flexibility by
learning to represent the world as a nested hierarchy of concepts, with each concept defined in
relation to simpler concepts, and more abstract representations computed in terms of less abstract
ones.
.
Difference between Machine Learning and Deep Learning :
Machine Learning Deep Learning
Works on small amount of Dataset for accuracy. Works on Large amount of Dataset.
Dependent on Low-end Machine. Heavily dependent on High-end Machine.
Divides the tasks into sub-tasks, solves them
individually and finally combine the results. Solves problem end to end.
Takes less time to train. Takes longer time to train.
Testing time may increase. Less time to test the data.
BY
B SARITHA 4
MACHINE LEARNING
UNIT – I
Introduction: Introduction to Machine learning, Supervised learning, Unsupervised learning,
Reinforcement learning. Deep learning.
Feature Selection: Filter, Wrapper , Embedded methods.
Feature Normalization :-min-max normalization, z-score normalization, and constant factor
normalization
Introduction to Dimensionality Reduction : Principal Component Analysis(PCA), Linear
Discriminant Analysis(LDA)
What is Machine Learning?
Machine Learning is a concept which allows the machine to learn from examples and experience,
and that too without being explicitly programmed. So instead of you writing the code, what you do
is you feed data to the generic algorithm, and the algorithm/ machine builds the logic based on the
given data.
How does Machine Learning Work?
Machine Learning algorithm is trained using a training data set to create a model. When new input
data is introduced to the ML algorithm, it makes a prediction on the basis of the model.The
prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning
algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained
again and again with an augmented training data set
BY
B SARITHA 1
,MACHINE LEARNING LECTURE NOTES
Types of Machine Learning
Machine learning is sub-categorized to three types:
Supervised Learning – Train Me!
Unsupervised Learning – I am self sufficient in learning
Reinforcement Learning – My life My rules! (Hit & Trial)
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We
have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model
gets trained it can start making a prediction or decision when new data is given to it.
What is Unsupervised Learning?
The model learns through observation and finds structures in the data. Once the model is given a
dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What
it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will
separate all the apples from mangoes.
Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based
on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now
if a new data is fed to the model, it adds it to one of the created clusters.
What is Reinforcement Learning?
It is the ability of an agent to interact with the environment and find out what is the best outcome. It
follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a
correct or a wrong answer, and on the basis of the positive reward points gained the model trains
itself. And again once trained it gets ready to predict the new data presented to it.
BY
B SARITHA 2
,MACHINE LEARNING LECTURE NOTES
Deep Learning:
Deep learning is a subfield of machine learning that deals with algorithms inspired by the structure
and function of the brain. Deep learning is a subset of machine learning, which is a part of artificial
intelligence (AI).
Deep Learning is a subset of Machine Learning that is based on artificial neural networks (ANNs)
with multiple layers, also known as deep neural networks (DNNs). These neural networks are inspired
by the structure and function of the human brain, and they are designed to learn from large amounts
of data in an unsupervised or semi-supervised manner.
Deep Learning models are able to automatically learn features from the data, which makes them well-
suited for tasks such as image recognition, speech recognition, and natural language processing. The
most widely used architectures in deep learning are feedforward neural networks, convolutional
neural networks (CNNs), and recurrent neural networks (RNNs).
BY
B SARITHA 3
, MACHINE LEARNING LECTURE NOTES
Feedforward neural networks (FNNs) are the simplest type of ANN, with a linear flow of information
through the network. FNNs have been widely used for tasks such as image classification, speech
recognition, and natural language processing.
Convolutional Neural Networks (CNNs) are a special type of FNNs designed specifically for image
and video recognition tasks. CNNs are able to automatically learn features from the images, which
makes them well-suited for tasks such as image classification, object detection, and image
segmentation.
Recurrent Neural Networks (RNNs) are a type of neural networks that are able to process sequential
data, such as time series and natural language. RNNs are able to maintain an internal state that
captures information about the previous inputs, which makes them well-suited for tasks such as
speech recognition, natural language processing, and language translation.
Deep Learning models are trained using large amounts of labeled data and require significant
computational resources. With the increasing availability of large amounts of data and computational
resources, deep learning has been able to achieve state-of-the-art performance in a wide range of
applications such as image and speech recognition, natural language processing, and more.
Deep learning is a particular kind of machine learning that achieves great power and flexibility by
learning to represent the world as a nested hierarchy of concepts, with each concept defined in
relation to simpler concepts, and more abstract representations computed in terms of less abstract
ones.
.
Difference between Machine Learning and Deep Learning :
Machine Learning Deep Learning
Works on small amount of Dataset for accuracy. Works on Large amount of Dataset.
Dependent on Low-end Machine. Heavily dependent on High-end Machine.
Divides the tasks into sub-tasks, solves them
individually and finally combine the results. Solves problem end to end.
Takes less time to train. Takes longer time to train.
Testing time may increase. Less time to test the data.
BY
B SARITHA 4
