Case study on machine learning

Case Study
on
“Machine Learning”
Submitted by:-
CS17033 P. Saundarya Rao
CS17034 Rohit Pagote
CS17035 Mahima Pimple
CS17037 Pratik Chaudhari
CS17041 Rajat C Ghatode
CS17042 Pratik Ramteke
CS17043 Samriddhi Shrivastava
Under the Guidance of:-
Prof. Rashmi Dagde
Department of Computer Science & Engineering
S. B. Jain Institute of Technology, Management and
Research, Nagpur – 441501
2020 – 2021

CONTENTS
SR. NO TOPIC PAGE NO.
1 INTROCDUCTION 1
2 HISTORY 3
3 TERMS RELATED TO MACHINE LEARNING 5
4 PROCESS AND STEPS INVOLVED 6
5 TYPES OF MACHINE LEARNING 9
6 TYPES OF PROBLEMS IN MACHINE LEARNING 12
7 CONCEPT OF DEEP LEARNING 13
8 APPLICATIONS 15
9 ADVANTAGES & DISADVANTAGES 17
10 CONCLUSION 19

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INTRODUCTION
Machine Learning is the most in-demand technology in today’s market. Its applications range from
self-driving cars to predicting deadly diseases such as ALS. The term Machine Learning was first coined
by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms
of technological advancements.
If you browse through the net about ‘what is Machine Learning’, you’ll get at least 100 different
definitions. However, the very first formal definition was given by Tom M. Mitchell:
“A computer program is said to learn from experience E with respect to some class of tasks T and
performance measure P if its performance at tasks in T, as measured by P, improves with experience E.”
In simple terms, Machine learning is a subset of Artificial Intelligence (AI) which provides
machines the ability to learn automatically & improve from experience without being explicitly
programmed to do so. In the sense, it is the practice of getting Machines to solve problems by gaining
the ability to think. If you feed a machine a good amount of data, it will learn how to interpret, process
and analyze this data by using Machine Learning Algorithms, in order to solve real-world problems.
Need for Machine Learning
Ever since the technical revolution, we’ve been generating an immeasurable amount of data. As per
research, we generate around 2.5 quintillion bytes of data every single day! It is estimated that by 2020,
1.7MB of data will be created every second for every person on earth.
With the availability of so much data, it is finally possible to build predictive models that can study
and analyze complex data to find useful insights and deliver more accurate results.
Importance of Machine Learning

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Top Tier companies such as Netflix and Amazon build such Machine Learning models by using
tons of data in order to identify profitable opportunities and avoid unwanted risks. Reasons why Machine
Learning is so important, are given below:
 Increase in Data Generation: Due to excessive production of data, we need a method that can
be used to structure, analyze and draw useful insights from data. This is where Machine Learning
comes in. It uses data to solve problems and find solutions to the most complex tasks faced by
organizations.
 Improve Decision Making: By making use of various algorithms, Machine Learning can be
used to make better business decisions. For example, Machine Learning is used to forecast sales,
predict downfalls in the stock market, identify risks and anomalies, etc.
 Uncover patterns & trends in data: Finding hidden patterns and extracting key insights from
data is the most essential part of Machine Learning. By building predictive models and using
statistical techniques, Machine Learning allows you to dig beneath the surface and explore the
data at a minute scale. Understanding data and extracting patterns manually will take days,
whereas Machine Learning algorithms can perform such computations in less than a second.
 Solve complex problems: From detecting the genes linked to the deadly ALS disease to building
self-driving cars, Machine Learning can be used to solve the most complex problems.
Flow of Machine Learning Process

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HISTORY
Early Stages
The first case of neural networks was in 1943, when neurophysiologist Warren McCulloch and
mathematician Walter Pitts wrote a paper about neurons, and how they work. They decided to create a
model of this using an electrical circuit, and therefore the neural network was born.
In 1950, Alan Turing created the world-famous Turing Test. This test is fairly simple - for a
computer to pass, it has to be able to convince a human that it is a human and not a computer.
1952 saw the first computer program which could learn as it ran. It was a game which played checkers,
created by Arthur Samuel.
Frank Rosenblatt designed the first artificial neural network in 1958, called Perceptron. The main
goal of this was pattern and shape recognition.
Another extremely early instance of a neural network came in 1959, when Bernard Widrow and
Marcian Hoff created two models of them at Stanford University. The first was called ADELINE, and
it could detect binary patterns. For example, in a stream of bits, it could predict what the next one would
be. The next generation was called MADELINE, and it could eliminate echo on phone lines, so had a
useful real world application. It is still in use today.
Despite the success of MADELINE, there was not much progress until the late 1970s for many
reasons, mainly the popularity of the Von Neumann architecture. This is an architecture where
instructions and data are stored in the same memory, which is arguably simpler to understand than a
neural network, and so many people built programs based on this.
1980s and 1990s
1982 was the year in which interest in neural networks started to pick up again, when John Hopfield
suggested creating a network which had bidirectional lines, similar to how neurons actually work.
Furthermore, in 1982, Japan announced it was focusing on more advanced neural networks, which
incentivized American funding into the area, and thus created more research in the area.
Neural networks use back propagation and this important step came in 1986, when three researchers
from the Stanford psychology department decided to extend an algorithm created by Widrow and Hoff

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in 1962. This therefore allowed multiple layers to be used in a neural network, creating what are known
as ‘slow learners’, which will learn over a long period of time.
The late 1980s and 1990s did not bring much to the field. However in 1997, the IBM computer
Deep Blue, which was a chess-playing computer, beat the world chess champion. Since then, there have
been many more advances in the field, such as in 1998, when research at AT&T Bell Laboratories on
digit recognition resulted in good accuracy in detecting handwritten postcodes from the US Postal
Service.
21st Century
Since the start of the 21st century, many businesses have realised that machine learning will increase
calculation potential. This is why they are researching more heavily in it, in order to stay ahead of the
competition. Some large projects include:
 GoogleBrain (2012)
 AlexNet (2012)
 DeepFace (2014)
 DeepMind (2014)
 OpenAI (2015)
 Amazon Machine Learning Platform (2015)
 ResNet (2015)
 U-net (2015)

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TERMS RELATED TO MACHINE LEARNING
Algorithm: A Machine Learning algorithm is a set of rules and statistical techniques used to learn
patterns from data and draw significant information from it. It is the logic behind a Machine Learning
model. An example of a Machine Learning algorithm is the Linear Regression algorithm.
Model: A model is the main component of Machine Learning. A model is trained by using a
Machine Learning Algorithm. An algorithm maps all the decisions that a model is supposed to take
based on the given input, in order to get the correct output.
Predictor Variable: It is a feature(s) of the data that can be used to predict the output.
Response Variable: It is the feature or the output variable that needs to be predicted by using the
predictor variable(s).
Training Data: The Machine Learning model is built using the training data. The training data
helps the model to identify key trends and patterns essential to predict the output.
Testing Data: After the model is trained, it must be tested to evaluate how accurately it can predict
an outcome. This is done by the testing data set.
Parts of Machine Learning

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PROCESS AND STEPS INVOLVED
The Machine Learning process involves building a Predictive model that can be used to find a
solution for a Problem Statement. To understand the Machine Learning process let’s assume that you
have been given a problem that needs to be solved by using Machine Learning.
Steps Involved in Machine Learning Process
The problem is to predict the occurrence of rain in your local area by using Machine Learning.
The below steps are followed in a Machine Learning process:
Step 1: Define the objective of the Problem Statement
At this step, we must understand what exactly needs to be predicted. In our case, the objective is to
predict the possibility of rain by studying weather conditions. At this stage, it is also essential to take
mental notes on what kind of data can be used to solve this problem or the type of approach you must
follow to get to the solution.
Step 2: Data Gathering
At this stage, you must be asking questions such as,
 What kind of data is needed to solve this problem?
 Is the data available?
 How can I get the data?

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Once you know the types of data that is required, you must understand how you can derive this
data. Data collection can be done manually or by web scraping. However, if you’re a beginner and
you’re just looking to learn Machine Learning you don’t have to worry about getting the data. There are
1000s of data resources on the web, you can just download the data set and get going.
Coming back to the problem at hand, the data needed for weather forecasting includes measures
such as humidity level, temperature, pressure, locality, whether or not you live in a hill station, etc. Such
data must be collected and stored for analysis.
Step 3: Data Preparation
The data you collected is almost never in the right format. You will encounter a lot of
inconsistencies in the data set such as missing values, redundant variables, duplicate values, etc.
Removing such inconsistencies is very essential because they might lead to wrongful computations and
predictions. Therefore, at this stage, you scan the data set for any inconsistencies and you fix them then
and there.
Step 4: Exploratory Data Analysis
Grab your detective glasses because this stage is all about diving deep into data and finding all the
hidden data mysteries. EDA or Exploratory Data Analysis is the brainstorming stage of Machine
Learning. Data Exploration involves understanding the patterns and trends in the data. At this stage, all
the useful insights are drawn and correlations between the variables are understood.
For example, in the case of predicting rainfall, we know that there is a strong possibility of rain if
the temperature has fallen low. Such correlations must be understood and mapped at this stage.
Step 5: Building a Machine Learning Model
All the insights and patterns derived during Data Exploration are used to build the Machine
Learning Model. This stage always begins by splitting the data set into two parts, training data, and
testing data. The training data will be used to build and analyze the model. The logic of the model is
based on the Machine Learning Algorithm that is being implemented.
In the case of predicting rainfall, since the output will be in the form of True (if it will rain
tomorrow) or False (no rain tomorrow), we can use a Classification Algorithm such as Logistic
Regression.

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Choosing the right algorithm depends on the type of problem you’re trying to solve, the data set
and the level of complexity of the problem. In the upcoming sections, we will discuss the different types
of problems that can be solved by using Machine Learning.
Step 6: Model Evaluation & Optimization
After building a model by using the training data set, it is finally time to put the model to a test. The
testing data set is used to check the efficiency of the model and how accurately it can predict the
outcome. Once the accuracy is calculated, any further improvements in the model can be implemented
at this stage. Methods like parameter tuning and cross-validation can be used to improve the performance
of the model.
Step 7: Predictions
Once the model is evaluated and improved, it is finally used to make predictions. The final output
can be a Categorical variable (eg. True or False) or it can be a Continuous Quantity (eg. the predicted
value of a stock).
In our case, for predicting the occurrence of rainfall, the output will be a categorical variable. So
that was the entire Machine Learning process. Now it’s time to learn about the different ways in which
Machines can learn.

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TYPES OF MACHINE LEARNING
A machine can learn to solve a problem by following any one of the following three approaches.
These are the ways in which a machine can learn:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
Supervised Learning
Supervised learning is a technique in which we teach or train the machine using data which is well
labeled. Supervised learning is a method by which you can use labeled training data to train a function
that you can then generalize for new examples. The training involves a critic that can indicate when the
function is correct or not, and then alter the function to produce the correct result. Classical examples
include neural networks that are trained by the back-propagation algorithm, but many other algorithms
exist. In supervised learning, you create a function (or model) by using labeled training data that consists
of input data and a wanted output. The supervision comes in the form of the wanted output, which in
turn lets you adjust the function based on the actual output it produces. When trained, you can apply this
function to new observations to produce an output (prediction or classification) that ideally responds
correctly. The supervised learning algorithm uses a labeled data set to produce a model. You can then
use this model with new data to validate the model’s accuracy or in production with live data.
To understand Supervised Learning let’s consider an analogy. As kids we all needed guidance to
solve math problems. Our teachers helped us understand what addition is and how it is done. Similarly,
you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled
data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing
but the training data set.
Consider the above figure. Here we’re feeding the machine images of Tom and Jerry and the goal
is for the machine to identify and classify the images into two groups (Tom images and Jerry images).
The training data set that is fed to the model is labeled, as in, we’re telling the machine, ‘this is how
Tom looks and this is Jerry’. By doing so you’re training the machine by using labeled data. In
Supervised Learning, there is a well-defined training phase done with the help of labeled data.

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Supervised Learning
Unsupervised Learning
Unsupervised learning involves training by using unlabeled data and allowing the model to act on
that information without guidance. In unsupervised learning, an algorithm segregates the data in a data
set in which the data is unlabeled based on some hidden features in the data. This function can be useful
for discovering the hidden structure of data and for tasks like anomaly detection. This tutorial explains
the ideas behind unsupervised learning and its applications, and then illustrates these ideas in the context
of exploring data.
Unsupervised Learning
Unsupervised learning algorithms group the data in an unlabeled data set based on the underlying
hidden features in the data. Because there are no labels, there’s no way to evaluate the result (a key
difference of supervised learning algorithms). By grouping data through unsupervised learning, you

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learn something about the raw data that likely wasn’t visible otherwise. In highly dimensional or large
data sets, this problem is even more pronounced.
Reinforcement Learning
Reinforcement Learning is a part of Machine learning where an agent is put in an environment and
he learns to behave in this environment by performing certain actions and observing the rewards which
it gets from those actions.
Reinforcement learning is an interesting learning model, with the ability not just to learn how to
map an input to an output but to map a series of inputs to outputs with dependencies (Markov decision
processes, for example). Reinforcement learning exists in the context of states in an environment and
the actions possible at a given state. During the learning process, the algorithm randomly explores the
state–action pairs within some environment (to build a state–action pair table), then in practice of the
learned information exploits the state–action pair rewards to choose the best action for a given state that
lead to some goal state.
Reinforcement Learning is mainly used in advanced Machine Learning areas such as self-driving
cars, AplhaGo, etc.
Reinforcement Learning

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TYPES OF PROBLEMS IN MACHINE LEARNING
Type of Problems Solved Using Machine Learning
Consider the above figure, there are three main types of problems that can be solved in Machine
Learning:
1. Regression: In this type of problem the output is a continuous quantity. So, for example, if you
want to predict the speed of a car given the distance, it is a Regression problem. Regression
problems can be solved by using Supervised Learning algorithms like Linear Regression.
2. Classification: In this type, the output is a categorical value. Classifying emails into two classes,
spam and non-spam is a classification problem that can be solved by using Supervised Learning
classification algorithms such as Support Vector Machines, Naive Bayes, Logistic Regression,
K Nearest Neighbour, etc.
3. Clustering: This type of problem involves assigning the input into two or more clusters based
on feature similarity. For example, clustering viewers into similar groups based on their interests,
age, geography, etc. can be done by using Unsupervised Learning algorithms like K-Means
Clustering.

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CONCEPT OF DEEP LEARNING
Deep learning
Deep learning is a subset of machine learning, which is essentially a neural network with three or
more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from
matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a
single layer can still make approximate predictions, additional hidden layers can help to optimize and
refine for accuracy.
Deep learning drives many artificial intelligence (AI) applications and services that improve
automation, performing analytical and physical tasks without human intervention. Deep learning
technology lies behind everyday products and services (such as digital assistants, voice-enabled TV
remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).
Deep learning vs. Machine learning
If deep learning is a subset of machine learning, how do they differ? Deep learning distinguishes
itself from classical machine learning by the type of data that it works with and the methods in which it
learns.
Machine learning algorithms leverage structured, labeled data to make predictions—meaning that
specific features are defined from the input data for the model and organized into tables. This doesn’t
necessarily mean that it doesn’t use unstructured data; it just means that if it does, it generally goes
through some pre-processing to organize it into a structured format.
Deep learning eliminates some of data pre-processing that is typically involved with machine
learning. These algorithms can ingest and process unstructured data, like text and images, and it
automates feature extraction, removing some of the dependency on human experts. For example, let’s
say that we had a set of photos of different pets, and we wanted to categorize by “cat”, “dog”, “hamster”,
et cetera. Deep learning algorithms can determined which features (e.g. ears) are most important to
distinguish each animal from another. In machine learning, this hierarchy of features is established
manually by a human expert.

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Then, through the processes of gradient descent and backpropagation, the deep learning algorithm
adjusts and fits itself for accuracy, allowing it to make predictions about a new photo of an animal with
increased precision.
Machine learning and deep learning models are capable of different types of learning as well, which
are usually categorized as supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning utilizes labeled datasets to categorize or make predictions; this requires some kind
of human intervention to label input data correctly. In contrast, unsupervised learning doesn’t require
labeled datasets, and instead, it detects patterns in the data, clustering them by any distinguishing
characteristics. Reinforcement learning is a process in which a model learns to become more accurate
for performing an action in an environment based on feedback in order to maximize the reward

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APPLICATIONS
As noted at the outset, machine learning is everywhere. Here are just a few examples of machine
learning you might encounter every day:
 Digital assistants: Apple Siri, Amazon Alexa, Google Assistant, and other digital assistants are
powered by natural language processing (NLP), a machine learning application that enables
computers to process text and voice data and 'understand' human language the way people do.
Natural language processing also drives voice-driven applications like GPS and speech
recognition (speech-to-text) software.
 Recommendations: Deep learning models drive 'people also liked' and 'just for you'
recommendations offered by Amazon, Netflix, Spotify, and other retail, entertainment, travel,
job search, and news services.
 Contextual online advertising: Machine learning and deep learning models can evaluate the
content of a web page—not only the topic, but nuances like the author's opinion or attitude—and
serve up advertisements tailored to the visitor's interests.
 Chatbots: Chatbots can use a combination of pattern recognition, natural language processing,
and deep neural networks to interpret input text and provide suitable responses.
 Fraud detection: Machine learning regression and classification models have replaced rules-
based fraud detection systems, which have a high number of false positives when flagging stolen
credit card use and are rarely successful at detecting criminal use of stolen or compromised
financial data.
 Cybersecurity: Machine learning can extract intelligence from incident reports, alerts, blog
posts, and more to identify potential threats, advise security analysts, and accelerate response.
 Medical image analysis: The types and volume of digital medical imaging data have exploded,
leading to more available information for supporting diagnoses but also more opportunity for
human error in reading the data. Convolutional neural networks (CNNs), recurrent neural

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networks (RNNs), and other deep learning models have proven increasingly successful at
extracting features and information from medical images to help support accurate diagnoses.
 Self-driving cars: Self-driving cars require a machine learning tour de force—they must
continuously identify objects in the environment around the car, predict how they will change or
move, and guide the car around the objects as well as toward the driver's destination. Virtually
every form of machine learning and deep learning algorithm mentioned above plays some role
in enabling a self-driving automobile.

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ADVANTAGES & DISADVANTAGES
Every coin has two faces, each face has its own property and features. It’s time to uncover the faces of
ML. A very powerful tool that holds the potential to revolutionize the way things work.
Advantages of Machine learning:
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. For instance, for an e-commerce website like
Amazon, it serves to understand the browsing behaviours and purchase histories of its users to
help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal
relevant advertisements to them.
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. A common example of this is anti-virus software’s; they learn to filter
new threats as they are recognized. ML is also good at recognizing spam.
3. Continuous Improvement:
As ML algorithms gain experience, they keep improving in accuracy and efficiency. This
lets them make better decisions. Say you need to make a weather forecast model. As the amount
of data you have keeps growing, your algorithms learn to make more accurate predictions faster.

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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 Machine Learning
With all those advantages to its powerfulness and popularity, Machine Learning isn’t perfect. The
following factors serve to limit it:
1. Data Acquisition:
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.
2. Time and Resources:
ML needs enough time to let the algorithms learn and develop enough to fulfil 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.
3. Interpretation of Results:
Another major challenge is the ability to accurately interpret results generated by the
algorithms. You must also carefully choose the algorithms for your purpose.
4. High error-susceptibility:
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. In the case of ML, such blunders can set off a chain of errors that can go undetected
for long periods of time. And when they do get noticed, it takes quite some time to recognize the
source of the issue, and even longer to correct it.

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CONCLUSION
Machine learning addresses the question of how to build computers that improve automatically
through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection
of computer science and statistics, and at the core of artificial intelligence and data science. Recent
progress in machine learning has been driven both by the development of new learning algorithms and
theory and by the ongoing explosion in the availability of online data and low-cost computation. The
adoption of data-intensive machine-learning methods can be found throughout science, technology and
commerce, leading to more evidence-based decision-making across many walks of life, including health
care, manufacturing, education, financial modeling, policing, and marketing.
As a result, we have studied Advantages and Disadvantages of Machine Learning. Also, this case
study helps an individual to understand why one needs to choose machine learning. While Machine
Learning can be incredibly powerful when used in the right ways and in the right places (where massive
training data sets are available), it certainly isn’t for everyone.

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REFERENCES
 https://www.edureka.co/blog/introduction-to-machine-learning/
 https://www.doc.ic.ac.uk/~jce317/history-machine-
learning.html#:~:text=1952%20saw%20the%20first%20computer,was%20pattern%20and%20
shape%20recognition
 https://data-flair.training/blogs/advantages-and-disadvantages-of-machine-learning/
 https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.geeksforgeeks.org/int
roduction-machine-
learning/amp/&ved=2ahUKEwjx9vrK27bwAhXj4nMBHdo1B50QFjARegQIDhAC&usg=AO
vVaw1IWT7HeKgE7A8TXYMdQAbk&ampcf=1
 https://www.ibm.com/cloud/learn/machine-learning
 https://en.wikipedia.org/wiki/Machine_learning
 https://www.internetsociety.org/resources/doc/2017/artificial-intelligence-and-machine-
learning-policy-
paper/?gclid=Cj0KCQjwytOEBhD5ARIsANnRjVg0ByYNC7wrTbspA5PNmLeZr-
CCsWTrKWwwcLp4dNr14FJaJfH7X9kaAnrbEALw_wcB

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