UNIT III SUPERVISED LEARNING 9
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
Introduction to Machine Learning
Can a machine also learn from experiences or past data like a human does? So here comes the role
of Machine Learning.
Introduction to Machine Learning
A subset of artificial intelligence known as machine learning focuses primarily on the creation of
algorithms that enable a computer to independently learn from data and previous experiences.
Arthur Samuel first used the term "machine learning" in 1959. It could be summarized as follows:
Without being explicitly programmed, machine learning enables a machine to automatically learn from
data, improve performance from experiences, and predict things.
For the purpose of developing predictive models, machine learning brings together statistics and
computer science.
,Algorithms that learn from historical data are either constructed or utilized in machine learning. The
performance will rise in proportion to the quantity of information we provide.
A machine can learn if it can gain more data to improve its performance.
How does Machine Learning work
A machine learning system builds prediction models, learns from previous data, and predicts the
output of new data whenever it receives it.
The amount of data helps to build a better model that accurately predicts the output, which in turn
affects the accuracy of the predicted output.
Let's say we have a complex problem in which we need to make predictions. Instead of writing code,
we just need to feed the data to generic algorithms, which build the logic based on the data and predict
the output.
Our perspective on the issue has changed as a result of machine learning. The Machine Learning
algorithm's operation is depicted in the following block diagram:
Features of Machine Learning:
o Machine learning uses data to detect various patterns in a given dataset.
o It can learn from past data and improve automatically.
o It is a data-driven technology.
o Machine learning is much similar to data mining as it also deals with the huge amount of the
data.
Need for Machine Learning
o Rapid increment in the production of data
, o Solving complex problems, which are difficult for a human
o Decision making in various sector including finance
o Finding hidden patterns and extracting useful information from data.
Classification of Machine Learning
At a broad level, machine learning can be classified into three types:
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
1) Supervised Learning
The system uses labelled data to build a model that understands the datasets and learns about each one.
After the training and processing are done, we test the model with sample data to see if it can
accurately predict the output.
The mapping of the input data to the output data is the objective of supervised learning. Spam filtering
is an example of supervised learning.
Supervised learning can be grouped further in two categories of algorithms:
o Classification
o Regression
, 2) Unsupervised Learning
Unsupervised learning is a learning method in which a machine learns without any supervision.
The training is provided to the machine with the set of data that has not been labelled, classified, or
categorized, and the algorithm needs to act on that data without any supervision.
The goal of unsupervised learning is to restructure the input data into new features or a group of
objects with similar patterns.
In unsupervised learning, we don't have a predetermined result. The machine tries to find useful
insights from the huge amount of data. It can be further classifieds into two categories of algorithms:
o Clustering
o Association
3) Reinforcement Learning
Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward
for each right action and gets a penalty for each wrong action.
The agent learns automatically with these feedbacks and improves its performance.
In reinforcement learning, the agent interacts with the environment and explores it. The goal of an
agent is to get the most reward points, and hence, it improves its performance.
The robotic dog, which automatically learns the movement of his arms, is an example of
Reinforcement learning.
History of Machine Learning
Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of
our daily life.
Machine learning is making our day to day life easy from self-driving cars to Amazon virtual
assistant "Alexa".
However, the idea behind machine learning is so old and has a long history. Below some milestones
are given which have occurred in the history of 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
Introduction to Machine Learning
Can a machine also learn from experiences or past data like a human does? So here comes the role
of Machine Learning.
Introduction to Machine Learning
A subset of artificial intelligence known as machine learning focuses primarily on the creation of
algorithms that enable a computer to independently learn from data and previous experiences.
Arthur Samuel first used the term "machine learning" in 1959. It could be summarized as follows:
Without being explicitly programmed, machine learning enables a machine to automatically learn from
data, improve performance from experiences, and predict things.
For the purpose of developing predictive models, machine learning brings together statistics and
computer science.
,Algorithms that learn from historical data are either constructed or utilized in machine learning. The
performance will rise in proportion to the quantity of information we provide.
A machine can learn if it can gain more data to improve its performance.
How does Machine Learning work
A machine learning system builds prediction models, learns from previous data, and predicts the
output of new data whenever it receives it.
The amount of data helps to build a better model that accurately predicts the output, which in turn
affects the accuracy of the predicted output.
Let's say we have a complex problem in which we need to make predictions. Instead of writing code,
we just need to feed the data to generic algorithms, which build the logic based on the data and predict
the output.
Our perspective on the issue has changed as a result of machine learning. The Machine Learning
algorithm's operation is depicted in the following block diagram:
Features of Machine Learning:
o Machine learning uses data to detect various patterns in a given dataset.
o It can learn from past data and improve automatically.
o It is a data-driven technology.
o Machine learning is much similar to data mining as it also deals with the huge amount of the
data.
Need for Machine Learning
o Rapid increment in the production of data
, o Solving complex problems, which are difficult for a human
o Decision making in various sector including finance
o Finding hidden patterns and extracting useful information from data.
Classification of Machine Learning
At a broad level, machine learning can be classified into three types:
1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
1) Supervised Learning
The system uses labelled data to build a model that understands the datasets and learns about each one.
After the training and processing are done, we test the model with sample data to see if it can
accurately predict the output.
The mapping of the input data to the output data is the objective of supervised learning. Spam filtering
is an example of supervised learning.
Supervised learning can be grouped further in two categories of algorithms:
o Classification
o Regression
, 2) Unsupervised Learning
Unsupervised learning is a learning method in which a machine learns without any supervision.
The training is provided to the machine with the set of data that has not been labelled, classified, or
categorized, and the algorithm needs to act on that data without any supervision.
The goal of unsupervised learning is to restructure the input data into new features or a group of
objects with similar patterns.
In unsupervised learning, we don't have a predetermined result. The machine tries to find useful
insights from the huge amount of data. It can be further classifieds into two categories of algorithms:
o Clustering
o Association
3) Reinforcement Learning
Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward
for each right action and gets a penalty for each wrong action.
The agent learns automatically with these feedbacks and improves its performance.
In reinforcement learning, the agent interacts with the environment and explores it. The goal of an
agent is to get the most reward points, and hence, it improves its performance.
The robotic dog, which automatically learns the movement of his arms, is an example of
Reinforcement learning.
History of Machine Learning
Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of
our daily life.
Machine learning is making our day to day life easy from self-driving cars to Amazon virtual
assistant "Alexa".
However, the idea behind machine learning is so old and has a long history. Below some milestones
are given which have occurred in the history of machine learning:
