Linear algebra provides the tools needed for machine learning algorithms by allowing complex operations to be described using matrices and vectors. It is widely used in machine learning because operations can be parallelized efficiently. Linear algebra also provides the foundation and notation used in other fields like calculus and probability that are important for machine learning. Machine learning involves feeding training data to algorithms that produce mathematical models to make predictions without being explicitly programmed. It works by learning from experience to improve performance at tasks over time. There are various applications of machine learning like image recognition, speech recognition, recommendations, and fraud detection.

1. CHAITANYA DEEMED TO BE UNIVERSITY
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
BTECH CSE IV YEAR I SEMESTER – MACHINE LEARNING
I. LINEAR ALGEBRA
Linear algebra will give you the tools to help you with the other areas of mathematics required to
understand and build better intuitions for machine learning algorithms.
What is Linear Algebra:
Linear Algebra is a branch of mathematics that concisely describes the coordinates and interactions of
planes in higher dimensions and performs operations on them. Think of it as an extension of algebra into
an arbitrary number of dimensions. Linear Algebra is about working on linear systems of equations Rather
than working with scalars, we start working with vectors and matrices. In linear algebra data is represented
in the form of linear equations. These linear equations are in turn represented in the form of matrices and
vectors.
How is Linear Algebra used in Machine Learning?
As a field, it is useful to you because you can describe complex operations used in machine learning using
the notation and formalisms from linear algebra. Linear algebra finds widespread application because it
generally parallelizes extremely well. Further to that most linear algebra operations can be implemented
without messaging passing which makes them amenable to MapReduce implementations.
Why is Linear Algebra a prerequisite behind modern scientific/computational research?
Linear Algebra is a foundation field that is to say that the notation and formalisms are used by other
branches of mathematics to express concepts that are also relevant to machine learning.
For example, matrices and vectors are used in calculus, needed when you want to talk about function
derivatives when optimizing a loss function. They are also used in probability when you want to talk about
statistical inference.

3. II. BASICS
Machine Learning is getting computers to program themselves. If programming is automation, then
machine learning is automating the process of automation. Writing software is the bottleneck, we don’t
have enough good developers. Let the data do the work instead of people. Machine learning is the way to
make programming scalable.
Traditional Programming: Data and program is run on the computer to produce the output.
Machine Learning: Data and output is run on the computer to create a program. This program can be used
in traditional programming. Machine learning is like farming or gardening. A seed is the algorithms, a
nutrient is the data, the gardener is you and plants are the programs.
III. LEARNING SYSTEM:
Machine Learning enables a Machine to automatically learn from Data, Improve performance from an
Experience and predict things without explicitly programmed.
In Simple Words, When we fed the Training Data to Machine Learning Algorithm, this algorithm will
produce a mathematical model and with the help of the mathematical model, the machine will make a
prediction and take a decision without being explicitly programmed. Also, during training data, the more
machine will work with it the more it will get experience and the more it will get experience the more
efficient result is produced.
Example : In Driverless Car, the training data is fed to Algorithm like how to Drive Car in Highway, Busy
and Narrow Street with factors like speed limit, parking, stop at signal etc. After that, a Logical and

4. Mathematical model is created on the basis of that and after that, the car will work according to the logical
model. Also, the more data is fed the more efficient output is produced.
Designing a Learning System in Machine Learning:
A computer program is said to be learning from experience (E), with respect to some task (T). Thus, the
performance measure (P) is the performance at task T, which is measured by P, and it improves with
experience E.”
Example: In Spam E-Mail detection,
 Task, T: To classify mails into Spam or Not Spam.
 Performance measure, P: Total percent of mails being correctly classified as being “Spam” or “Not
Spam”.
 Experience, E: Set of Mails with label “Spam”
Steps for Designing Learning System are:
Step 1) Choosing the Training Experience: The very important and first task is to choose the training
data or training experience which will be fed to the Machine Learning Algorithm. It is important to note
that the data or experience that we fed to the algorithm must have a significant impact on the Success or
Failure of the Model. So Training data or experience should be chosen wisely.
Below are the attributes which will impact on Success and Failure of Data:

5.  The training experience will be able to provide direct or indirect feedback regarding choices.
 For example: While Playing chess the training data will provide feedback to itself like instead of
this move if this is chosen the chances of success increases.
 Second important attribute is the degree to which the learner will control the sequences of training
examples.
 For example: when training data is fed to the machine then at that time accuracy is very less but
when it gains experience while playing again and again with itself or opponent the machine
algorithm will get feedback and control the chess game accordingly.
 Third important attribute is how it will represent the distribution of examples over which
performance will be measured.
 For example, a Machine learning algorithm will get experience while going through a number of
different cases and different examples. Thus, Machine Learning Algorithm will get more and
more experience by passing through more and more examples and hence its performance will
increase.
Step 2- Choosing target function: The next important step is choosing the target function. It means
according to the knowledge fed to the algorithm the machine learning will choose NextMove function
which will describe what type of legal moves should be taken.
 For example : While playing chess with the opponent, when opponent will play then the
machine learning algorithm will decide what be the number of possible legal moves taken in
order to get success.
Step 3- Choosing Representation for Target function: When the machine algorithm will know all the
possible legal moves the next step is to choose the optimized move using any representation i.e. using
linear Equations, Hierarchical Graph Representation, Tabular form etc. The NextMove function will move
the Target move like out of these move which will provide more success rate.
 For Example : while playing chess machine have 4 possible moves, so the machine will choose
that optimized move which will provide success to it.
Step 4- Choosing Function Approximation Algorithm: An optimized move cannot be chosen just with
the training data. The training data had to go through with set of example and through these examples the
training data will approximates which steps are chosen and after that machine will provide feedback on it.
 For Example : When a training data of Playing chess is fed to algorithm so at that time it is not
machine algorithm will fail or get success and again from that failure or success it will measure
while next move what step should be chosen and what is its success rate.

6. Step 5- Final Design: The final design is created at last when system goes from number of examples ,
failures and success , correct and incorrect decision and what will be the next step etc.
 Example: DeepBlue is an intelligent computer which is ML-based won chess game against the
chess expert Garry Kasparov, and it became the first computer which had beaten a human
chess expert.
IV. GOALS AND APPLICATIONS OF ML:
 Machine learning uses data to detect various patterns in a given dataset.
 It can learn from past data and improve automatically.
 It is a data-driven technology.
 Machine learning is much similar to data mining as it also deals with the huge amount of the
data.
 Rapid increment in the production of data
 Solving complex problems, which are difficult for a human
 Decision making in various sector including finance
 Finding hidden patterns and extracting useful information from data.
Applications
 Web search: ranking page based on what you are most likely to click on.
 Computational biology: rational design drugs in the computer based on past experiments.
 Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How
to decide where to invest money.
 E-commerce: Predicting customer churn. Whether or not a transaction is fraudulent.
 Space exploration: space probes and radio astronomy.

7.  Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
 Information extraction: Ask questions over databases across the web.
 Social networks: Data on relationships and preferences. Machine learning to extract value from
data.
 Debugging: Use in computer science problems like debugging. Labor intensive process. Could
suggest where the bug could be.
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:
 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.
 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.
 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.

8.  Product recommendations:
Machine learning is widely used by various e-commerce and entertainment companies such
as 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.
 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.
 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 spam box, and
the technology behind this is Machine learning. Below are some spam filters used by Gmail:
 Content Filter
 Header filter
 General blacklists filter
 Rules-based filters
 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.
 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 can help us in
various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an
appointment, etc.

9. 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.
 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.
 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.
 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.
 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.

10. V. CLASSIFICATION OF ML:
At a broad level, machine learning can be classified into three types:
 Supervised learning
 Unsupervised learning
 Reinforcement learning
Supervised Learning
Supervised learning is a type of machine learning method in which we provide sample labeled data to the
machine learning system in order to train it, and on that basis, it predicts the output.
The system creates a model using labeled data to understand the datasets and learn about each data, once
the training and processing are done then we test the model by providing a sample data to check whether
it is predicting the exact output or not.
The goal of supervised learning is to map input data with the output data. The supervised learning is based
on supervision, and it is the same as when a student learns things in the supervision of the teacher. The
example of supervised learning is spam filtering. Supervised learning can be grouped further in two
categories of algorithms:
o Classification
o Regression
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 labeled, 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

11. 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:
 Clustering
 Association
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.