SIDDARTHA INSTITUTE OF SCIENCE AND TECHNOLOGY:: PUTTUR

(AUTONOMOUS) L T P C
3 - - 3
III B.Tech. – II Sem.

(20CS0535) MACHINE LEARNING

(Professional Elective Course-II)

COURSE OBJECTIVES

The objectives of this course:

1. To investigate various Supervised Learning models of machine learning
2. To investigate various Unsupervised Learning models of machine learning
3. To investigate various Reinforcement Learning models of machine learning
4. To expose students to the Dimensionality Reduction

COURSE OUTCOMES (COs)

On successful completion of this course, the student will be able to
1. Understand the basics of Machine Learning
2. Apply the various supervised learning algorithms to classification and regression
problems
3. Analyze the various unsupervised learning techniques like k-means, EM algorithm and to
apply for real world problems
4. Understand the concepts of Clustering Techniques.
5. Identify the need of Parametric methods and Dimensionality Reduction Techniques in
machine learning.
6. Infer the theoretical and practical concepts of Reinforcement Learning
UNIT-I

INTRODUCTION: What is machine learning? -Examples of machine learning applications- Types of

machine learning. –Model selection and generalization – Guidelines for Machine LearningExperiments

UNIT-II

SUPERVISED LEARNING: Classification, Decision Trees – Univariate Tree –Multivariate Tree –

Pruning, Bayesian Decision Theory, Parametric Methods-Maximum Likelihood Estimation -Evaluating
an Estimator Bias and Variance -The Bayes‟ Estimator, Linear Discrimination- Gradient Descent-
Logistic Discrimination-Discrimination by Regression, Multilayer Perceptron-Perceptron- Multilayer
Perceptrons- Back Propagation Algorithm

UNIT-III

UNSUPERVISED LEARNING: clustering- Introduction- Mixture Densities- k-Means Clustering-

Expectation-Maximization Algorithm- Mixtures of Latent Variable Models- Supervised Learning after
Clustering- Hierarchical Clustering
UNIT-IV

NONPARAMETRIC METHODS- Nonparametric Density Estimation- k-Nearest

NeighborEstimator- Nonparametric Classification- Condensed Nearest Neighbor

DIMENSIONALITY REDUCTION-Subset Selection-Principal Components Analysis- Factor

Analysis- Multidimensional Scaling-Linear Discriminant Analysis.

UNIT-V

REINFORCEMENT LEARNING: Introduction- Single State Case:K-Armed Bandit- Elements of

Reinforcement Learning- Model- Based Learning- Temporal DifferenceLearning- Generalization-
Partially Observable States

TEXT BOOKS

1. Ethem Alpaydin, Introduction to Machine Learning,MIT Press, Second Edition,2010.

REFERENCES

1. Tom M Mitchell, Machine Learning, First Edition, McGraw Hill Education, 2013

2. Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction.MIT

Press
 UNIT-1

UNIT-I

INTRODUCTION: What is machine learning? -Examples of machine learning applications- Types of

machine learning. –Model selection and generalization – Guidelines for Machine LearningExperiments

Machine learning is a growing technology which enables computers to learn automatically from past
data. Machine learning uses various algorithms for building mathematical models and making
predictions using historical data or information. Currently, it is being used for various tasks such
as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender
system, and many more.

What is Machine Learning?

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. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it
in a summarized way as:

Machine learning enables a machine to automatically learn from data, improve performance from
experiences, and predict things without being explicitly programmed.
With the help of sample historical data, which is known as training data, machine learning algorithms
build a mathematical model that helps in making predictions or decisions without being explicitly
programmed. Machine learning brings computer science and statistics together for creating predictive
models. Machine learning constructs or uses the algorithms that learn from historical data. The more we
will provide the information, the higher will be the performance.

A machine has the ability to learn if it can improve its performance by gaining more data.

Machine learning is a subfield of artificial intelligence that involves training computers to learn from
data without being explicitly programmed. In other words, machine learning algorithms use statistical
techniques to find patterns in data and use these patterns to make predictions or take actions.

How does Machine Learning work

A Machine Learning system learns from historical data, builds the prediction models, and whenever
it receives new data, predicts the output for it. 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.

Suppose we have a complex problem, where we need to perform some predictions, so instead of writing
a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms,
machine builds the logic as per the data and predict the output. Machine learning has changed our way
of thinking about the problem. The below block diagram explains the working of Machine Learning
algorithm:

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

The need for machine learning is increasing day by day. The reason behind the need for machine learning
is that it is capable of doing tasks that are too complex for a person to implement directly. Asa human,
we have some limitations as we cannot access the huge amount of data manually, so for this, we need
some computer systems and here comes the machine learning to make things easy for us.
We can train machine learning algorithms by providing them the huge amount of data and let them
explore the data, construct the models, and predict the required output automatically. The performance
of the machine learning algorithm depends on the amount of data, and it can be determined by the cost
function. With the help of machine learning, we can save both time and money.

The importance of machine learning can be easily understood by its uses cases, Currently, machine
learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion
by Facebook, etc. Various top companies such as Netflix and Amazon have build machine learning
models that are using a vast amount of data to analyze the user interest and recommend product
accordingly.

Following are some key points which show the importance of 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.

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:

The early history of Machine Learning (Pre-1940):

o 1834: In 1834, Charles Babbage, the father of the computer, conceived a device that could be
programmed with punch cards. However, the machine was never built, but all modern computers
rely on its logical structure.
 o 1936: In 1936, Alan Turing gave a theory that how a machine can determine and execute a set
of instructions.

The era of stored program computers:

o 1940: In 1940, the first manually operated computer, "ENIAC" was invented, which was the first
electronic general-purpose computer. After that stored program computer such as EDSAC in
1949 and EDVAC in 1951 were invented.
o 1943: In 1943, a human neural network was modeled with an electrical circuit. In 1950, the
scientists started applying their idea to work and analyzed how human neurons might work.

Computer machinery and intelligence:

o 1950: In 1950, Alan Turing published a seminal paper, "Computer Machinery and
Intelligence," on the topic of artificial intelligence. In his paper, he asked, "Can machines
think?"

Machine intelligence in Games:

o 1952: Arthur Samuel, who was the pioneer of machine learning, created a program that helped
an IBM computer to play a checkers game. It performed better more it played.
o 1959: In 1959, the term "Machine Learning" was first coined by Arthur Samuel.

The first "AI" winter:

o The duration of 1974 to 1980 was the tough time for AI and ML researchers, and this duration
was called as AI winter.
o In this duration, failure of machine translation occurred, and people had reduced their interest
from AI, which led to reduced funding by the government to the researches.

Machine Learning from theory to reality

o 1959: In 1959, the first neural network was applied to a real-world problem to remove echoes
over phone lines using an adaptive filter.
o 1985: In 1985, Terry Sejnowski and Charles Rosenberg invented a neural network NETtalk,
which was able to teach itself how to correctly pronounce 20,000 words in one week.
o 1997: The IBM's Deep blue intelligent computer won the chess game against the chess expert
Garry Kasparov, and it became the first computer which had beaten a human chess expert.

Machine Learning at 21st century

o 2006: In the year 2006, computer scientist Geoffrey Hinton has given a new name to neural net
research as "deep learning," and nowadays, it has become one of the most trending technologies.
 o 2012: In 2012, Google created a deep neural network which learned to recognize the image of
humans and cats in YouTube videos.
o 2014: In 2014, the Chabot "Eugen Goostman" cleared the Turing Test. It was the first Chabot
who convinced the 33% of human judges that it was not a machine.
o 2014: DeepFace was a deep neural network created by Facebook, and they claimed that it
could recognize a person with the same precision as a human can do.
o 2016: AlphaGo beat the world's number second player Lee sedol at Go game. In 2017 it beat
the number one player of this game Ke Jie.
o 2017: In 2017, the Alphabet's Jigsaw team built an intelligent system that was able to learn
the online trolling. It used to read millions of comments of different websites to learn to stop
online trolling.

Machine Learning at present:

Now machine learning has got a great advancement in its research, and it is present everywhere around
us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It
includes Supervised, unsupervised, and reinforcement learning with
clustering, classification, decision tree, SVM algorithms, etc.

Modern machine learning models can be used for making various predictions, including weather
prediction, disease prediction, stock market analysis, etc.

Prerequisites

Before learning machine learning, you must have the basic knowledge of followings so that you can
easily understand the concepts of machine learning:

o Fundamental knowledge of probability and linear algebra.

o The ability to code in any computer language, especially in Python language.
o Knowledge of Calculus, especially derivatives of single variable and multivariate functions.

• interact with it.

Examples of Machine Learning Applications

Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day. We
are using machine learning in our daily life even without knowing it such as Google Maps, Google
assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:
1. Image Recognition:

Image recognition is one of the most common applications of machine learning. It is used to identify
objects, persons, places, digital images, etc. The popular use case of image recognition and face detection
is, Automatic friend tagging suggestion:

Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our
Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind
this is machine learning's face detection and recognition algorithm.

It is based on the Facebook project named "Deep Face," which is responsible for face recognition and
person identification in the picture.

2. Speech Recognition

While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's
a popular application of machine learning.

Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech
to text", or "Computer speech recognition." At present, machine learning algorithms are widely used
by various applications of speech recognition. Google assistant, Siri, Cortana, and Alexa are
using speech recognition technology to follow the voice instructions.

3. Traffic prediction:

If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the
shortest route and predicts the traffic conditions.

It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested
with the help of two ways:

o Real Time location of the vehicle form Google Map app and sensors
o Average time has taken on past days at the same time.

Everyone who is using Google Map is helping this app to make it better. It takes information from the
user and sends back to its database to improve the performance.
4. Product recommendations:

Machine learning is widely used by various e-commerce and entertainment companies suchas
Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product
on Amazon, then we started getting an advertisement for the same product while internet surfing on the
same browser and this is because of machine learning.

Google understands the user interest using various machine learning algorithms and suggests the product
as per customer interest.

As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc.,
and this is also done with the help of machine learning.

5. Self-driving cars:

One of the most exciting applications of machine learning is self-driving cars. Machine learning plays
a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on
self-driving car. It is using unsupervised learning method to train the car models to detect people and
objects while driving.

6. Email Spam and Malware Filtering:

Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always
receive an important mail in our inbox with the important symbol and spam emails in our spambox, and
the technology behind this is Machine learning. Below are some spam filters used by Gmail:

o Content Filter
o Header filter
o General blacklists filter
o Rules-based filters
o Permission filters

Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes
classifier are used for email spam filtering and malware detection.

7. Virtual Personal Assistant:

We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name
suggests, they help us in finding the information using our voice instruction. These assistants canhelp us
in various ways just by our voice instructions such as Play music, call someone, Open an email,
Scheduling an appointment, etc.

These virtual assistants use machine learning algorithms as an important part.

These assistant record our voice instructions, send it over the server on a cloud, and decode it using ML
algorithms and act accordingly.
8. Online Fraud Detection:

Machine learning is making our online transaction safe and secure by detecting fraud transaction.
Whenever we perform some online transaction, there may be various ways that a fraudulent transaction
can take place such as fake accounts, fake ids, and steal money in the middle of a transaction. So to
detect this, Feed Forward Neural network helps us by checking whether it is a genuine transaction or
a fraud transaction.

For each genuine transaction, the output is converted into some hash values, and these values become
the input for the next round. For each genuine transaction, there is a specific pattern which gets change
for the fraud transaction hence, it detects it and makes our online transactions more secure.

9. Stock Market trading:

Machine learning is widely used in stock market trading. In the stock market, there is always a risk of
up and downs in shares, so for this machine learning's long short term memory neural network is used
for the prediction of stock market trends.

10. Medical Diagnosis:

In medical science, machine learning is used for diseases diagnoses. With this, medical technology is
growing very fast and able to build 3D models that can predict the exact position of lesions in the
brain. It helps in finding brain tumors and other brain-related diseases easily.

11. Automatic Language Translation:

Nowadays, if we visit a new place and we are not aware of the language then it is not a problem at all,
as for this also machine learning helps us by converting the text into our known languages. Google's
GNMT (Google Neural Machine Translation) provide this feature, which is a Neural Machine Learning
that translates the text into our familiar language, and it called as automatic translation.

The technology behind the automatic translation is a sequence to sequence learning algorithm, which
is used with image recognition and translates the text from one language to another language.

Types of Machine Learning

At a broad level, machine learning can be classified into three types:

1. Supervised learning 2.Unsupervised learning 3.Reinforcement learning

Supervised Machine Learning

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 meanssome 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. It applies the same concept as a student learns in the
supervision of the teacher.

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 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.
The working of Supervised learning can be easily understood by the below example and diagram:

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.

o If the given shape has four sides, and all the sides are equal, then it will be labelled as
a Square.
o If the given shape has three sides, then it will be labelled as a triangle.
o 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:

o First Determine the type of training dataset
o Collect/Gather the labelled training data.
o Split the training dataset into training dataset, test dataset, and validation dataset.
o Determine the input features of the training dataset, which should have enough knowledge so
that the model can accurately predict the output.
o Determine the suitable algorithm for the model, such as support vector machine, decision tree,
etc.
o Execute the algorithm on the training dataset. Sometimes we need validation sets as the control
parameters, which are the subset of training datasets.
o 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:

Supervised learning can be further divided into two types of problems:

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:

o Linear Regression
o Regression Trees
o Non-Linear Regression
 o Bayesian Linear Regression
o Polynomial Regression

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,

o Random Forest
o Decision Trees
o Logistic Regression
o Support vector Machines

Advantages of Supervised learning:

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

Disadvantages of supervised learning:

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

Unsupervised Machine Learning

In the previous topic, we learned supervised machine learning in which models are trained using labeled
data under the supervision of training data. But there may be many cases in which we do not have labeled
data and need to find the hidden patterns from the given dataset. So, to solve such typesof cases in
machine learning, we need unsupervised learning techniques.

What is Unsupervised Learning?

Unsupervised learning is a machine learning technique in which models are not supervised using training
dataset. Instead, models itself find the hidden patterns and insights from the given data. It can be
compared to learning which takes place in the human brain while learning new things. It can be defined
as:
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 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.

Example: Suppose the unsupervised learning algorithm is given an input dataset containing images of
different types of cats and dogs. The algorithm is never trained upon the given dataset, which means it
does not have any idea about the features of the dataset. The task of the unsupervised learning algorithm
is to identify the image features on their own. Unsupervised learning algorithm will perform this task by
clustering the image dataset into the groups according to similarities between images.

Keep Watching

Why use Unsupervised Learning?

Below are some main reasons which describe the importance of Unsupervised Learning:

o Unsupervised learning is helpful for finding useful insights from the data.
o Unsupervised learning is much similar as a human learns to think by their own experiences,
which makes it closer to the real AI.
o Unsupervised learning works on unlabeled and uncategorized data which make unsupervised
learning more important.
o 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

Working of unsupervised learning can be understood by the below diagram:

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:

The unsupervised learning algorithm can be further categorized into two types of problems:

o 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.
o 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:
Below is the list of some popular unsupervised learning algorithms:
o K-means clustering
o KNN (k-nearest neighbors)
o Hierarchal clustering
o Anomaly detection
o Neural Networks
o Principle Component Analysis
o Independent Component Analysis
o Apriority algorithm
o Singular value decomposition

Advantages of Unsupervised Learning

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

Disadvantages of Unsupervised Learning

o Unsupervised learning is intrinsically more difficult than supervised learning as it does not
have corresponding output.
o The result of the unsupervised learning algorithm might be less accurate as input data is not
labeled, and algorithms do not know the exact output in advance.

Difference between Supervised and Unsupervised Learning

Supervised and Unsupervised learning are the two techniques of machine learning. But both the
techniques are used in different scenarios and with different datasets. Below the explanation of both
learning methods along with their difference table is given.
The main differences between Supervised and Unsupervised learning are given below:

Supervised Learning Unsupervised Learning

Supervised learning algorithms are trained Unsupervised learning algorithms are

using labeled data. trained using unlabeled data.

Supervised learning model takes direct Unsupervised learning model does not take
feedback to check if it is predicting correct any feedback.
output or not.

Supervised learning model predicts the output. Unsupervised learning model finds the
hidden patterns in data.

In supervised learning, input data is provided In unsupervised learning, only input data is
to the model along with the output. provided to the model.

The goal of supervised learning is to train the The goal of unsupervised learning is to find
model so that it can predict the output when it the hidden patterns and useful insights from
is given new data. the unknown dataset.

Supervised learning needs supervision to train Unsupervised learning does not need any
the model. supervision to train the model.

Supervised learning can be categorized Unsupervised Learning can be classified

in Classification and Regression problems. in Clustering and Associations problems.
 Supervised learning can be used for those cases Unsupervised learning can be used for those
where we know the input as well as cases where we have only input data and no
corresponding outputs. corresponding output data.

Supervised learning model produces an Unsupervised learning model may give less
accurate result. accurate result as compared to supervised
learning.

Supervised learning is not close to true Unsupervised learning is more close to the
Artificial intelligence as in this, we first train true Artificial Intelligence as it learns
the model for each data, and then only it can similarly as a child learns daily routine things
predict the correct output. by his experiences.

It includes various algorithms such as Linear It includes various algorithms such as

Regression, Logistic Regression, Support Clustering, KNN, and Apriori algorithm.
Vector Machine, Multi-class Classification,
Decision tree, Bayesian Logic, etc.

Reinforcement learning:

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to

maximize reward in a particular situation. It is employed by various software and machines to find
the best possible behavior or path it should take in a specific situation. Reinforcement learning
differs from supervised learning in a way that in supervised learning the training data has the answer
key with it so the model is trained with the correct answer itself whereas in reinforcement learning,
there is no answer but the reinforcement agent decides what to do to perform the given task. In the
absence of a training dataset, it is bound to learn from its experience.
Example: The problem is as follows: We have an agent and a reward, with many hurdles in
between. The agent is supposed to find the best possible path to reach the reward. The following
problem explains the problem more easily.

The above image shows the robot, diamond, and fire. The goal of the robot is to get the reward that
is the diamond and avoid the hurdles that are fired. The robot learns by trying all the possible paths
and then choosing the path which gives him the reward with the least hurdles. Each right step will
give the robot a reward and each wrong step will subtract the reward of the robot. The total reward
will be calculated when it reaches the final reward that is the diamond.
Main points in Reinforcement learning –

• Input: The input should be an initial state from which the model will start
• Output: There are many possible outputs as there are a variety of solutions to a particular
problem
• Training: The training is based upon the input, The model will return a state and the user will
decide to reward or punish the model based on its output.
• The model keeps continues to learn.
• The best solution is decided based on the maximum reward.

Difference between Reinforcement learning and Supervised learning:

Reinforcement learning Supervised learning

Reinforcement learning is all about making decisions

sequentially. In simple words, we can say that the output In Supervised learning, the
depends on the state of the current input and the next input decision is made on the initial
depends on the output of the previous input input or the input given at the start

In supervised learning the

decisions are independent of each
In Reinforcement learning decision is dependent, So we give other so labels are given to each
labels to sequences of dependent decisions decision.

Example: Chess game Example: Object recognition

Types of Reinforcement: There are two types of Reinforcement:

1. Positive –
Positive Reinforcement is defined as when an event, occurs due to a particular behavior,
increases the strength and the frequency of the behavior. In other words, it has a positive effect
on behavior.
Advantages of reinforcement learning are:
• Maximizes Performance
• Sustain Change for a long period of time
• Too much Reinforcement can lead to an overload of states which can diminish the
results
2. Negative –
Negative Reinforcement is defined as strengthening of behavior because a negative condition is
stopped or avoided.
Advantages of reinforcement learning:
• Increases Behavior
• Provide defiance to a minimum standard of performance
• It Only provides enough to meet up the minimum behavior
Various Practical applications of Reinforcement Learning –

• RL can be used in robotics for industrial automation.

• RL can be used in machine learning and data processing
 • RL can be used to create training systems that provide custom instruction and materials
according to the requirement of students.
• RL can be used in large environments in the following situations:
A model of the environment is known, but an analytic solution is not available;
• Only a simulation model of the environment is given (the subject of simulation-based
optimization)
Model Selection and Generalization

Model selection refers to the process of selecting the best model from a set of candidate models based
on their performance on a given task. This process typically involves splitting the available data into
training and validation sets, using the training set to train each candidate model, and then evaluating
their performance on the validation set. The model with the best performance on the validation set is
selected as the final model.

Fig:Model Selection and Generalization

Generalization refers to the ability of a model to perform well on new, unseen data. When a model is
trained on a dataset, it may overfitt the training data by memorizing specific patterns in the data that
are not representative of the underlying distribution. This can lead to poor performance on new data.
To ensure good generalization, it is important to evaluate a model's performance on a separate test set
that was not used during model selection or training.

To improve generalization, techniques such as regularization, early stopping, and data augmentation
can be used. Regularization involves adding a penalty term to the loss function to discourage complex
models that are prone to overfitting. Early stopping involves monitoring the validation error during
training and stopping the training process when the error begins to increase. Data augmentation
involves generating new training examples by applying transformations to existing examples, which
can increase the size and diversity of the training set and help prevent overfitting.

Overall, model selection and generalization are crucial aspects of machine learning that help ensure
that models are accurate and reliable, and can be applied successfully to new data.
 Fig:Model Seleciton

GUIDELINES FOR MACHINE LEARNING EXPERIMENTS

Machine Learning Steps

The task of imparting intelligence to machines seems daunting and impossible. But it is actually really
easy. It can be broken down into 7 major steps :

1. Collecting Data:

As you know, machines initially learn from the data that you give them. It is of the utmost importance
to collect reliable data so that your machine learning model can find the correct patterns. The quality of
the data that you feed to the machine will determine how accurate your model is. If you have incorrect
or outdated data, you will have wrong outcomes or predictions which are not relevant.

Make sure you use data from a reliable source, as it will directly affect the outcome of your model. Good
data is relevant, contains very few missing and repeated values, and has a good representation of the
various subcategories/classes present.
2. Preparing the Data:

After you have your data, you have to prepare it. You can do this by:

• Putting together all the data you have and randomizing it. This helps make sure that data is evenly
distributed, and the ordering does not affect the learning process.

• Cleaning the data to remove unwanted data, missing values, rows, and columns, duplicate values,
data type conversion, etc. You might even have to restructure the dataset and changethe rows
and columns or index of rows and columns.

• Visualize the data to understand how it is structured and understand the relationship between
various variables and classes present.

• Splitting the cleaned data into two sets - a training set and a testing set. The training set is the set
your model learns from. A testing set is used to check the accuracy of your model after training.
 Figure 3: Cleaning and Visualizing Data

3. Choosing a Model:

A machine learning model determines the output you get after running a machine learning algorithm
on the collected data. It is important to choose a model which is relevant to the task at hand. Over the
years, scientists and engineers developed various models suited for different tasks like speech
recognition, image recognition, prediction, etc. Apart from this, you also have to see if your model is
suited for numerical or categorical data and choose accordingly.

Figure 4: Choosing a model

4. Training the Model:

Training is the most important step in machine learning. In training, you pass the prepared data to your
machine learning model to find patterns and make predictions. It results in the model learning from the
data so that it can accomplish the task set. Over time, with training, the model gets better at predicting.

Figure 5: Training a model

5. Evaluating the Model:

After training your model, you have to check to see how it‟s performing. This is done by testing the
performance of the model on previously unseen data. The unseen data used is the testing set that you
split our data into earlier. If testing was done on the same data which is used for training, you will not
get an accurate measure, as the model is already used to the data, and finds the same patterns in it, as it
previously did. This will give you disproportionately high accuracy.

When used on testing data, you get an accurate measure of how your model will perform and its speed.
 Figure 6: Evaluating a model
6. Parameter Tuning:

Once you have created and evaluated your model, see if its accuracy can be improved in any way. This
is done by tuning the parameters present in your model. Parameters are the variables in the model that
the programmer generally decides. At a particular value of your parameter, the accuracy will be the
maximum. Parameter tuning refers to finding these values.

Figure 7: Parameter Tuning

7. Making Predictions
In the end, you can use your model on unseen data to make predictions accurately.
How to Implement Machine Learning Steps in Python?
You will now see how to implement a machine learning model using Python.
In this example, data collected is from an insurance company, which tells you the variables that come
into play when an insurance amount is set. Using this, you will have to predict the insurance amount
for a person. This data was collected from Kaggle.com, which has many reliable datasets.
You need to start by importing any necessary modules, as shown.

Figure 8: Importing necessary modules

Following this, you will import the data.

Figure 9: Importing data

 Figure 10: Insurance dataset

Now, clean your data by removing duplicate values, and transforming columns into numerical values
to make them easier to work with.

Figure 11: Cleaning Data

The final dataset becomes as shown.

 Figure 12: Cleaned dataset

Now, split your dataset into training and testing sets.

Figure 13: Splitting the dataset

As you need to predict a numeral value based on some parameters, you will have to use Linear
Regression. The model needs to learn on your training set. This is done by using the '.fit' command.

Figure 14: Choosing and training your model

Now, predict your testing dataset and find how accurate your predictions are.
 Figure 15: Predicting using your model

1.0 is the highest level of accuracy you can get. Now, get your parameters.

Figure 16: Model Parameters

The above picture shows the hyperparameters which affect the various variables in your dataset.
AI& ML Differences

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.

Below are some main differences between AI and machine learning along with the overview of Artificial
intelligence and machine learning

Artificial Intelligence

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. AI is being used in multiple places
such as Siri, Google?s AlphaGo, AI in Chess playing, etc.

Based on capabilities, AI can be classified into three types:

o Weak AI
o General AI
o Strong AI

Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is
said that it will be intelligent than humans.

Machine learning
Machine learning is about extracting knowledge from the data. It can be defined as,
 Machine learning is a subfield of artificial intelligence, which enables machines to learn from past
data or experiences without being explicitly programmed.

Artificial Intelligence Machine learning

Artificial intelligence is a technology which Machine learning is a subset of AI which allows a machine
enables a machine to simulate humanbehavior. to automatically learn from past data withoutprogramming
explicitly.

The goal of AI is to make a smart computer The goal of ML is to allow machines to learn from data so
system like humans to solve complex that they can give accurate output.
problems.

In AI, we make intelligent systems to perform In ML, we teach machines with data to perform a
any task like a human. particular task and give an accurate result.

Machine learning and deep learning are the Deep learning is a main subset of machine learning.
two main subsets of AI.

AI has a very wide range of scope. Machine learning has a limited scope.

AI is working to create an intelligent system Machine learning is working to create machines that can
which can perform various complex tasks. perform only those specific tasks for which they are trained.

AI system is concerned about maximizing the Machine learning is mainly concerned about accuracy and
chances of success. patterns.

The main applications of AI are Siri, customer The main applications of machine learning are Online
support using catboats, Expert System, recommender system, Google search
Online game playing, intelligent algorithms, Facebook auto friend tagging suggestions,
humanoid robot, etc. etc.

On the basis of capabilities, AI can be divided Machine learning can also be divided into mainly three
into three types, which are, Weak AI, General types that are Supervised learning, Unsupervised
AI, and Strong AI. learning, and Reinforcement learning.

It includes learning, reasoning, and self- It includes learning and self-correction when introduced
correction. with new data.

AI completely deals with Structured, semi- Machine learning deals with Structured and semi-
structured, and unstructured data. structured data.