A internship report on artificial intelligence

ARTIFICIAL INTILLIGENCE
An Internship Report
By
A. Geetha saranya(22JG5A0201)
Under the esteemed guidance of
Mr. Y. Ramu
Assistant Professor
EEE Department
Department of Electricals and Electronics Engineering
GAYATRIVIDYA PARISHAD COLLEGE OF ENGINEERING FOR WOMEN
[Approved by AICTE NEW DELHI, Affiliated to JNTUK Kakinada]
[Accredited by National Board of Accreditation for B.Tech. CSE, ECE & IT- Valid from 2019-22 2022-2025]
[Accredited by National Assessment and Accreditation Council (NAAC)– Valid from 2022-2027]
Komadi, MadhuraWada, Visakhapatnam–530048
2023-2024
GAYATRI VIDYA PARISHAD COLLEGE OF ENGINEERING FOR WOMEN

DEPARTMENT OF ELECTRICALAND ELECTRONICS ENGINEERING
CERTIFICATE
This is to certify that the internship report titled “ARTIFICIAL INTILLIGENCE” is a
Bonafide work of following III B.Tech. students in the Department of Information Technology, Gayatri
Vidya Parishad College of Engineering for Women affiliated to JNT University, Kakinada during the
academic year 2023-2024.
A. Geetha saranya(22JG5A0201)
Mr. Y. Ramu Dr. R. V. S. Laxmi kumari
Assistant Professor
(Internal Guide) (Head of the department

ACKNOLEDGEMENT
The satisfaction that accompanies the successful completion of any task would be incomplete
without the mention of people who made it possible and whose constant guidance and encouragement
crown all the efforts with success.
We feel elated to extend our sincere gratitude to Mr. Y. Ramu, Assistant Professor for
encouragement all the way during analysis of the project. His annotations, insinuations and
criticisms are the key behind the successful completion of the thesis and for providing us all the
required facilities.
We express our deep sense of gratitude and thanks to Dr. R. V.S. Laxmi Kumari, Professor
and Head of the Department of Information Technology for her guidance and for expressing her
valuable and grateful opinions in the project for its development and for providing lab sessions and
extra hours to complete the project.
We would like to take this opportunity to express our profound sense of gratitude to Vice Principal, Dr.
G. Sudheer for allowing us to utilize the college resources thereby facilitating the successful
completion of our project. We are also thankful to both teaching and non-teaching faculty of the
Department of Computer Science and Engineering for giving valuable suggestions for our project.
We would like to take the opportunity to express our profound sense of gratitude to the revered
Principal. R. K. Goswami for all the help and support towards the successful completion of our project

ABSTRACT
Artificial Intelligence (AI) is a transformative field that focuses on enabling machines to
simulate human intelligence, allowing them to learn, interpret, and make decisions
autonomously. This technology has already made significant strides in sectors like healthcare,
education, and software development, showcasing its potential to revolutionize industries and
improve efficiency. AI encompasses various algorithms and techniques that empower
machines to analyse data, adapt to circumstances, and deliver accurate results in diverse
scenarios.
This project explores the critical aspects of AI, including the training and evaluation of
Intelligent systems, assessing their performance, and determining their suitability for specific
problem domains. The primary objective is to deepen the understanding of AI algorithms,
implement them in real-world scenarios, and evaluate their outcomes to identify the most
effective solutions. As a rapidly advancing technology, AI holds immense potential to drive
innovation and transformation across multiple domains in the future

TABLE OF CONTENTS
1. Introduction………………………………………………………………………………07 .
2.1.3. Machine Learning Algorithms..........................................................................10
2.1.4. Applications of Machine Learning....................................................................11
2.2. Techniques of Machine Learning...............................................................................12
2.2.1. Supervised Learning..........................................................................................12
2.2.2. Unsupervised Learning......................................................................................16
2.2.3. Semi- supervised Learning................................................................................18
2.2.4. Reinforcement Learning....................................................................................19
2.2.5. Some Important Considerations in Machine Learning.....................................19
2.3. Data Preprocessing.....................................................................................................20
2.3.1. Data Preparation................................................................................................20
2.3.2. Feature Engineering..........................................................................................21
2.3.3. Feature Scaling..................................................................................................22
2.3.4. Datasets.............................................................................................................24
2.3.5. Dimensionality Reduction with Principal Component Analysis.......................24
2.4. Math Refresher...........................................................................................................25
2.4.1. Concept of Linear Algebra................................................................................25
2.4.2. Eigenvalues, Eigenvectors, and Eigen decomposition......................................30
2.4.3. Introduction to Calculus....................................................................................30
2.4.4. Probability and Statistics...................................................................................31
2.5. Supervised learning....................................................................................................34
2.5.1. Regression.........................................................................................................34
2.5.1.1. Linear Regression...................................................................................35
2.5.1.2. Multiple Linear Regression.....................................................................35
2.5.1.3. Polynomial Regression...........................................................................36
2.5.1.4. Decision Tree Regression.......................................................................37
2.5.1.5. Random Forest Regression.....................................................................37
2.5.2. Classification.....................................................................................................38
2.5.2.1. Linear Models.........................................................................................39
2.5.2.1.1. Logistic Regression........................................................................39
2.5.2.1.2. Support Vector machines...............................................................39
2.5.2.2. Nonlinear Models....................................................................................40
2.5.2.2.1. K-Nearest Neighbors (KNN).........................................................40
2.5.2.2.2. Kernel Support Vector Machines (SVM).......................................40
2.5.2.2.3. Naïve Bayes...................................................................................41
2.5.2.2.4. Decision Tree Classification..........................................................41
2.5.2.2.5. Random Forest Classification........................................................42
2.6. Unsupervised learning................................................................................................43
2.6.1. Clustering..........................................................................................................43

2.6.1.1. Clustering Algorithms.............................................................................43
2.6.1.2. K-means Clustering................................................................................44
2.7. Introduction to Deep Learning...................................................................................45
2.7.1. Meaning and Importance of Deep Learning.....................................................45
2.7.2. Artificial Neural Networks................................................................................46
2.7.3. TensorFlow........................................................................................................47
3. Reason for choosing Machine Learning.............................................................................47
4. Learning Outcome...............................................................................................................48
1.1. A Taste of Machine Learning……………………………………………………….07
1.2. Relation to Data Mining………………………………………………………….….07
1.3. Relation to Optimization………………………………………………………….…07
1.4. Relation to Statistics…………………………………………………………............08
1.5. Future of Machine Learning………………………………………………………....08
2. Technology Learnt……………………………………………………………………….08
2.1. Introduction to Artificial Intelligence and Machine Learning……………………....08
2.1.1. Definition of Artificial Intelligence…………………………………………..08
2.1.2. Definition of Machine Learning…………………………………………...…09
5. Gantt Chart……………………………………………………………………………….49
6. Bibliography……………………………………………………………………………...49
6.1. Content source……………………………………………………………………….49
6.2. Picture from………………………………………………………………………….49
6.3. Book referred………………………………………………………………………..49

1. Introduction
1.1. A Taste of Machine Learning
✓ Arthur Samuel, an American pioneer in the field of computer gaming and artificial
intelligence, coined the term "Machine Learning" in 1959.
✓ Over the past two decades Machine Learning has become one of the mainstays of
information technology.
✓ With the ever-increasing amounts of data becoming available there is good reason
to believe that smart data analysis will become even more pervasive as a
necessary ingredient for technological progress.
1.2. Relation to Data Mining
• Data mining uses many machine learning methods, but with different goals; on the
other hand, machine learning also employs data mining methods as "unsupervised
learning" or as a preprocessing step to improve learner accuracy.
1.3. Relation to Optimization

Machine learning also has intimate ties to optimization: many learning problems
are formulated as minimization of some loss function on a training set of
examples.
Loss functions express the discrepancy between the predictions of the model being
trained and the actual problem instances.
1.4. Relation to Statistics
field.
Leo Bierman distinguished two statistical modelling paradigms: data model and
algorithmic model, wherein "algorithmic model" means more or less the machine
learning algorithms like Random Forest.
1.5. Future of Machine Learning
➢ Machine Learning can be a competitive advantage to any company be it a top
MNC or a startup as things that are currently being done manually will be done
tomorrow by machines.
➢ Machine Learning revolution will stay with us for long and so will be the future of
Machine Learning.
2. Technology Learnt
2.1. Introduction to AI & Machine Learning
2.1.1. Definition of Artificial Intelligence
❖ Data Economy
✓ World is witnessing real time flow of all types structured and unstructured data
from social media, communication, transportation, sensors, and devices.
✓ International Data Corporation (IDC) forecasts that 180 zettabytes of data will
be generated by 2025.
a science as a placeholder to call the overall
Michael I. Jordan suggested the term dat

✓ This explosion of data has given rise to a new economy known as the Data
Economy.
✓ Data is the new oil that is precious but useful only when cleaned and processed.
✓ There is a constant battle for ownership of data between enterprises to derive
benefits from it.
❖ Define Artificial Intelligence
Artificial intelligence refers to the simulation of human intelligence in machines that are
programmed to think like humans and mimic their actions. The term may also be applied to
any machine that exhibits traits associated with a human mind such as learning and problem-
solving.
2.1.2. Definition of Machine Learning
❖ Relationship between AI and ML
Machine Learning is an approach or subset of Artificial Intelligence that is based on the idea
that machines can be given access to data along with the ability to learn from it.

❖ Define Machine Learning
Machine learning is an application of artificial intelligence (AI) that provides systems the
ability to automatically learn and improve from experience without being explicitly
programmed. Machine learning focuses on the development of computer programs that can
access data and use it learn for themselves.
❖ Features of Machine Learning
✓ Machine Learning is computing-intensive and generally requires a large amount of
training data.
✓ It involves repetitive training to improve the learning and decision making of
algorithms.
✓ As more data gets added, Machine Learning training can be automated for learning
new data patterns and adapting its algorithm.
✓ 2.1.3. Machine Learning Algorithms
❖ Traditional Programming vs. Machine Learning Approach
❖ Traditional Approach
Traditional programming relies on hard-coded rules.
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❖ Machine Learning Approach
Machine Learning relies on learning patterns based on sample data.
❖ Machine Learning Techniques
✓ Machine Learning uses a number of theories and techniques from Data
Science.
✓ Machine Learning can learn from labelled data (known as supervised learning)
or unlabeled data (known as unsupervised learning).
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2.1.4. Applications of Machine Learning
❖ Image Processing
✓ Optical Character Recognition (OCR)
✓ Self-driving cars
✓ Image tagging and recognition
❖ Robotics
✓ Industrial robotics
✓ Human simulation
❖ Data Mining
✓ Association rules
✓ Anomaly detection
✓ Grouping and Predictions
❖ Video games
✓ Pokémon
✓ PUBG
❖ Text Analysis
✓ Spam Filtering
✓ Information Extraction
✓ Sentiment Analysis
❖ Healthcare
✓ Emergency Room & Surgery
✓ Research
✓ Medical Imaging & Diagnostics
2.2. Techniques of Machine Learning
2.2.1. Supervised Learning
❖ Define Supervised Learning
Supervised learning is the machine learning task of learning a function that maps an input to
an output based on example input-output pairs. It infers a function from labeled training data
consisting of a set of training examples.
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In supervised learning, each example is a pair consisting of an input object (typically a vector)
and a desired output value (also called the supervisory signal).
❖ Supervised Learning Flow
✓ Data Preparation Clean data
Label data (x, y)
Feature Engineering
Reserve 80% of data for Training (Train_X) and
20% for Evaluation (Train_E)
✓ Training Step
Design algorithmic logic
Train the model with Train X
Derive the relationship between x and y, that is, y =
f(x)
✓ Evaluation or Test Step
Evaluate or test with Train E
If accuracy score is high, you have the final learned
algorithm y = f(x) If accuracy score is low, go back
to training step
✓ Production Deployment
Use the learned algorithm y = f(x) to predict production data.
The algorithm can be improved by more training data, capacity, or algo redesign.
❖ Testing the Algorithms
✓ Once the algorithm is trained, test it with test data (a set of data instances that
do not appear in the training set).
✓ A well-trained algorithm can predict well for new test data.
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✓ If the learning is poor, we have an underfitted situation. The algorithm will not
work well on test data. Retraining may be needed to find a better fit.
✓ If learning on training data is too intensive, it may lead to overfitting–a
situation where the algorithm is not able to handle new testing data that it has
not seen before. The technique to keep data generic is called regularization.
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❖ Examples of Supervised Learning
✓ Voice Assistants
✓ Gmail Filters
✓ Weather Apps
❖ Types of Supervised Learning
✓ Classification
➢ Answers “What class?”
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➢ Applied when the output has finite and discreet values Example: Social
media sentiment analysis has three potential outcomes, positive,
negative, or neutral
✓ Regression
➢ Answers “How much?”
➢ Applied when the output is a continuous number
➢ A simple regression algorithm: y = wx + b. Example: relationship between
environmental temperature (y) and humidity levels (x)
2.2.2. Unsupervised Learning
❖ Define Unsupervised Learning
Unsupervised learning is the training of machine using information that is neither classified
nor labeled and allowing the algorithm to act on that information without guidance.
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Here the task of machine is to group unsorted information according to similarities, patterns
and differences without any prior training of data.
❖ Types of Unsupervised Learning
✓ Clustering
The most common unsupervised learning method is cluster analysis. It is used to find data
clusters so that each cluster has the most closely matched data.
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✓ Visualization Algorithms
Visualization algorithms are unsupervised learning algorithms that accept unlabeled data and
display this data in an intuitive 2D or 3D format. The data is separated into somewhat clear
clusters to aid understanding.
✓ Anomaly Detection
This algorithm detects anomalies in data without any prior training.
2.2.3. Semi- supervised Learning
❖ Define Semi-supervised Learning
Semi-supervised learning is a class of machine learning tasks and techniques that also make
use of unlabeled data for training – typically a small amount of labeled data with a large
amount of unlabeled data.
Semi-supervised learning falls between unsupervised learning (without any labeled training
data) and supervised learning (with completely labeled training data).
❖ Example of Semi-supervised Learning
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▪ Google Photos automatically detects the same person in multiple photos from a
vacation trip (clustering –unsupervised).
▪ One has to just name the person once (supervised), and the name tag gets
attached to that person in all the photos.
2.2.4. Reinforcement Learning
❖ Define Reinforcement Learning
Reinforcement Learning is a type of Machine Learning that allows the learning system to
observe the environment and learn the ideal behavior based on trying to maximize some
notion of cumulative reward.
It differs from supervised learning in that labelled input/output pairs need not be presented,
and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance
between exploration (of uncharted territory) and exploitation (of current knowledge)
❖ Features of Reinforcement Learning
• The learning system (agent) observes the environment, selects and takes certain
actions, and gets rewards in return (or penalties in certain cases).
• The agent learns the strategy or policy (choice of actions) that maximizes its
rewards over time.
❖ Example of Reinforcement Learning
• In a manufacturing unit, a robot uses deep reinforcement learning to identify a
device from one box and put it in a container.
• The robot learns this by means of a rewards-based learning system, which
incentivizes it for the right action.
2.2.5. Some Important Considerations in Machine Learning
❖ Bias & Variance Tradeoff
➢ Bias refers to error in the machine learning model due to wrong assumptions. A
high-bias model will underfit the training data.
➢ Variance refers to problems caused due to overfitting. This is a result of
oversensitivity of the model to small variations in the training data. A model
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with many degrees of freedom (such as a high-degree polynomial model) is
likely to have high variance and thus overfit the training data.
❖ Bias & Variance Dependencies
➢ Increasing a model’s complexity will reduce its bias and increase its variance.
➢ Conversely, reducing a model’s complexity will increase its bias and reduce its
variance. This is why it is called a trade-off.
❖ What is Representational Learning
In Machine Learning, Representation refers to the way the data is presented. This often make
a huge difference in understanding.
2.3. Data Preprocessing
2.3.1. Data Preparation
❖ Data Preparation Process
✓ Machine Learning depends largely on test data.
✓ Data preparation involves data selection, filtering, transformation, etc.
✓ Data preparation is a crucial step to make it suitable for ML.
✓ A large amount of data is generally required for the most common forms of
ML.
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❖ Types of Data
✓ Labelled Data or Training Data
✓ Unlabelled Data
✓ Test Data
✓ Validation Data
2.3.2. Feature Engineering
❖ Define Feature Engineering
The transformation stage in the data preparation process includes an important step known as
Feature Engineering.
Feature Engineering refers to selecting and extracting right features from the data that are
relevant to the task and model in consideration.
❖ Aspects of Feature Engineering
✓ Feature Selection
Most useful and relevant features are selected from the available data
✓ Feature Addition
New features are created by gathering new data
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✓ Feature Extraction
Existing features are combined to develop more useful ones
✓ Feature Filtering
Filter out irrelevant features to make the modelling step easy
2.3.3. Feature Scaling
❖ Define Feature Scaling
✓ Feature scaling is an important step in the data transformation stage of data
preparation process.
✓ Feature Scaling is a method used in Machine Learning for standardization of
independent variables of data features.
❖ Techniques of Feature Scaling
✓ Standardization
▪ Standardization is a popular feature scaling method, which gives data
the property of a standard normal distribution (also known as Gaussian
distribution).
▪ All features are standardized on the normal distribution (a mathematical
model).
▪ The mean of each feature is centered at zero, and the feature column
has a standard deviation of one.
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✓ Normalization
▪ In most cases, normalization refers to rescaling of data features between
0 and 1, which is a special case of Min-Max scaling.
▪ In the given equation, subtract the min value for each feature from each
feature instance and divide by the spread between max and min.
▪ In effect, it measures the relative percentage of distance of each
instance from the min value for that feature.
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2.3.4. Datasets
➢ Machine Learning problems often need training or testing datasets.
➢ A dataset is a large repository of structured data.
➢ In many cases, it has input and output labels that assist in Supervised Learning.
2.3.5. Dimensionality Reduction with Principal Component Analysis
❖ Define Dimensionality Reduction
✓ Dimensionality reduction involves transformation of data to new dimensions in
a way that facilitates discarding of some dimensions without losing any key
information.
❖ Define Principal Component Analysis (PCA)
✓ Principal component analysis (PCA) is a technique for dimensionality
reduction that helps in arriving at better visualization models.
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❖ Applications of PCA
✓ Noise reduction
✓ Compression
✓ Preprocess
2.4. Math Refresher
2.4.1. Concept of Linear Algebra
❖ Linear Equation
Linear algebra is a branch of mathematics that deals with the study of vectors
and linear functions and equations.
A linear equation does not involve any products, inverses, or roots of variables.
All variables occur only to the first power and not as arguments for
trigonometric, logarithmic, or exponential functions.
❖ System of Linear Equations
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A linear system that has a solution is called consistent, and the one with no
solution is termed inconsistent.
❖ Matrix
An m × n matrix: the m rows are horizontal and the n columns are vertical. Each element of a
matrix is often denoted by a variable with two subscripts. For example, a2,1 represents the
element at the second row and first column of the matrix.
✓ Addition
Two matrices can be added only if they have the same number of rows and columns. Also,
during addition, A + B = B + A
✓ Subtraction
Two matrices can be subtracted only if they have the same number of rows and columns. Also,
during subtraction, A -B not equal to B -A
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A system of linear equations is a finite collection of linear equations.

✓ Multiplication
The matrix product AB is defined only when the number of columns in A is equal to the
number of rows in B. BA is defined only when the number of columns in B is equal to the
number of rows in A. AB is not always equal to BA.
✓ Transpose
✓ Inverse
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❖ Special Types of Matrix
➢ Diagonal Matrix
➢ Symmetric Matrix
➢ Identity Matrix
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❖ Vector
A vector (v) is an object with both magnitude (length) and direction.
It starts from origin (0,0), and its length is denoted by ||v||.
➢ Addition
The operation of adding two or more vectors together into a vector sum
is referred to as vector addition.
➢ Subtraction
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Vector subtraction is the process of subtracting two or more vectors to
get a vector difference.
➢ Multiplication
Vector multiplication refers to a technique for the multiplication of two
(or more) vectors with themselves.
2.4.2. Eigenvalues, Eigenvectors, and Eigen decomposition
❖ Eigenvalue & Eigenvector
▪ An eigenvector of a square matrix A is a non-zero vector such that
multiplication by A alters only the scale of v.
❖ Eigen decomposition
▪ Integers can be broken into their prime factors to understand them, example: 12
= 2 x 2 x 3. From this, useful properties can be derived, for example, the
number is not divisible by 5 and is divisible by 2 and 3.
▪ Similarly, matrices can be decomposed. This will help you discover
information about the matrix.
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2.4.3. Introduction to Calculus
Calculus is the study of change. It provides a framework for modelling systems in which there
is change and ways to make predictions of such models.
❖ Differential Calculus
✓ Differential calculus is a part of calculus that deals with the study of the rates at
which quantities change.
✓ Let x and y be two real numbers such that y is a function of x, that is, y = f(x).
✓ If f(x) is the equation of a straight line (linear equation), then the equation is
represented as y = mx + b.
✓ Where m is the slope determined by the following equation:
❖ Integral Calculus
✓ Integral Calculus assigns numbers to functions to describe displacement, area,
volume, and other concepts that arise by combining infinitesimal data.
✓ Given a function f of a real variable x and an interval [a, b] of the real line, the
definite integral is defined informally as the signed area of the region in the
xyplane that is bounded by the graph of f, the x -axis, and the vertical lines x=a
and x=b.
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2.4.4. Probability and Statistics
❖ Probability Theory
➢ Probability is the measure of the likelihood of an event’s occurrence. ➢
Example: The chances of getting heads on a coin toss is ½ or 50%
➢ Probability of any specific event is between 0 and 1 (inclusive). The sum of
total probabilities of an event cannot exceed 1, that is, 0 <= p(x) <= 1. This
implies that. p(x)dx =1 (integral of p for a distribution over x)
❖ Conditional Probability
➢ Conditional Probability is a measure of the probability of an event occurring
given that another event has occurred.
❖ Chain Rule of Probability
➢ Joint probability distribution over many random variables may be decomposed
into conditional distributions over only one variable. ➢ It can be represented
as:
❖ Standard Deviance
➢ Standard deviation is a quantity that expresses the value by which the members
of a group differ from the mean value for the group.
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➢ Standard deviation is used more often than variance because the unit in which
it is measured is the same as that of mean, a measure of central tendency.
❖ Variance
➢ Variance refers to the spread of the data set, for example, how far the numbers
are in relation to the mean.
➢ Variance is particularly useful when calculating the probability of future events
or performance.
➢ Notice that variance is just the square of standard deviation.
❖ Covariance
➢ Covariance is the measure of how two random variables change together. It is
used to calculate the correlation between variables.
❖ Logistic Sigmoid
➢ The Logistic Sigmoid is a useful function that follows the S curve. It saturates
when input is very large or very small.
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❖ Gaussian Distribution
➢ The distribution where the data tends to be around a central value with lack of
bias or minimal bias toward the left or right is called Gaussian distribution, also
known as normal distribution.
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2.5. Supervised learning
2.5.1. Regression
✓ In statistical modeling, regression analysis is a set of statistical processes for
estimating the relationships among variables.
✓ It includes many techniques for modeling and analyzing several variables, when the
focus is on the relationship between a dependent variable and one or more independent
variables (or 'predictors').
✓ More specifically, regression analysis helps one understand how the typical value of
the dependent variable (or 'criterion variable') changes when any one of the
independent variables is varied, while the other independent variables are held fixed.
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2.5.1.1. Linear Regression
➢ Linear regression is a linear approach for modeling the relationship
between a scalar dependent variable y and an independent variable x.
➢ where x, y, w are vectors of real numbers and w is a vector of weight
parameters.
➢ The equation is also written as:
y = wx + b
➢ where b is the bias or the value of output for zero input
2.5.1.2. Multiple Linear Regression
It is a statistical technique used to predict the outcome of a response variable
through several explanatory variables and model the relationships between
them.
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2.5.1.3. Polynomial Regression
• Polynomial regression is applied when data is not formed in a straight line.
• It is used to fit a linear model to non-linear data by creating new features from
powers of non-linear features.
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It represents line fitment between multiple inputs and one output, typically:

2.5.1.4. Decision Tree Regression
o A decision tree is a graphical representation of all the possible solutions to
a decision based on a few conditions. o Decision Trees are non-parametric
models, which means that the number of parameters is not determined prior
to training. Such models will normally overfit data.
o In contrast, a parametric model (such as a linear model) has a
predetermined number of parameters, thereby reducing its degrees of
freedom. This in turn prevents overfitting.
o max_depth –limit the maximum depth of the tree
o min_samples_split –the minimum number of samples a node must have
before it can be split
o min_samples_leaf –the minimum number of samples a leaf node must
have o min_weight_fraction_leaf –same as min_samples_leaf but
expressed as a fraction of total instances
o max_leaf_nodes –maximum number of leaf nodes
o max_features –maximum number of features that are evaluated for
splitting at each node
2.5.1.5. Random Forest Regression
➢ Ensemble Learning uses the same algorithm multiple times or a group of
different algorithms together to improve the prediction of a model.
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➢ Random Forests use an ensemble of decision trees to perform regression
tasks.
2.5.2. Classification
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.
class
-
binomial and multi
There are 2 types of classification,
inite and discreet values.
It is best used when the output has f
It predicts a class for an input variable.
It specifies the class to which data elements belong to.

2.5.2.1. Linear Models
2.5.2.1.1. Logistic Regression
This method is widely used for binary classification problems. It can
also be extended to multi-class classification problems.
✓ A binary dependent variable can have only two values, like 0 or 1, win
or lose, pass or fail, healthy or sick, etc.
✓ The probability in the logistic regression is often represented by the
Sigmoid function (also called the logistic function or the S-curve)
2.5.2.1.2. Support Vector machines
➢ SVMs are very versatile and are also capable of performing linear or
nonlinear classification, regression, and outlier detection.
➢ They involve detecting hyperplanes which segregate data into classes.
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➢ The optimization objective is to find “maximum margin hyperplane” that
is farthest from the closest points in the two classes (these points are called
support vectors).
2.5.2.2. Nonlinear Models
2.5.2.2.1. K-Nearest Neighbours (KNN)
K-nearest Neighbours algorithm is used to assign a data point to clusters
based on similarity measurement.
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A new input point is classified in the category such that it has the
greatest number of neighbours from that category.
2.5.2.2.2. Kernel Support Vector Machines (SVM)
Kernel SVMs is used for classification of nonlinear data.
In the chart, nonlinear data is projected into a higher dimensional space via a
mapping function where it becomes linearly separable.
A reverse projection of the higher dimension back to original feature
space takes it back to nonlinear shape.
2.5.2.2.3. Naïve Bayes
▪ According to Bayes model, the conditional probability P(Y|X) can be
calculated as:
▪ This means you have to estimate a very large number of P(X|Y)
probabilities for a relatively small vector space X.
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2.5.2.2.4. Decision Tree Classification
✓ The advantage of decision trees is that they require very little data
preparation.
✓ They do not require feature scaling or centering at all.
✓ They are also the fundamental components of Random Forests, one of
the most powerful ML algorithms.
✓ Start at the tree root and split the data on the feature using the decision
algorithm, resulting in the largest information gain (IG).
2.5.2.2.5. Random Forest Classification
➢ Random decision forests correct for decision trees' habit of overfitting to
their training set.
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➢ Random forests or random decision forests are an ensemble learning
method for classification, regression and other tasks that operates by
constructing a multitude of decision trees at training time and outputting
the class that is the mode of the classes (classification) or mean prediction
(regression) of the individual trees.
2.6. Unsupervised learning
2.6.1. Clustering
2.6.1.1. Clustering Algorithms
❖ Clustering means
✓ Clustering is a Machine Learning technique that involves the grouping
of data points.
❖ Prototype Based Clustering
▪ Prototype-based clustering assumes that most data is located near
prototypes; example: centroids (average) or medoid (most frequently
occurring point)
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▪ K-means, a Prototype-based method, is the most popular method for
clustering that involves:
• Training data that gets assigned to matching cluster based on similarity
• Iterative process to get data points in the best clusters possible
2.6.1.2. K-means Clustering
❖ K-means Clustering Algorithm
Step 1: randomly pick k centroids
Step 2: assign each point to the nearest centroid
Step 3: move each centroid to the center of the
respective cluster
Step 4: calculate the distance of the centroids from
each point again
Step 5: move points across clusters and re-calculate the distance from the
centroid
Step 6: keep moving the points across clusters until the Euclidean
distance is minimized
❖ Elbow Method
➢ One could plot the Distortion against the number of clusters K. Intuitively, if K
increases, distortion should decrease. This is because the samples will be close
to their assigned centroids. This plot is called the Elbow method.
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➢ It indicates the optimum number of clusters at the position of the elbow, the
point where distortion begins to increase most rapidly.
❖ Euclidian Distance
✓ K-means is based on finding points close to cluster centroids. The
distance between two points x and y can be measured by the squared
Euclidean distance between them in an m-dimensional space.
❖ Examples of K-means Clustering
➢ Grouping articles (example: Google news)
➢ Grouping customers who share similar interests
➢ Classifying high risk and low risk patients from a patient pool
2.7. Introduction to Deep Learning
2.7.1. Meaning and Importance of Deep Learning
❖ Define Deep Learning
Deep Learning is a specialized form of Machine Learning that uses supervised, unsupervised,
or semi-supervised learning to learn data representations.
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It is similar to the structure and function of the human nervous system.
❖ Why Deep Learning
The vast availability of Big Data enables machines to be trained.
Experts have discovered multi-layered learning networks that can be
leveraged for deep learning as they learn in layers.
Scientists have figured out that high-performing graphics processing units (GPU)
can be used for deep learning.
❖ ML Vs Deep Learning
2.7.2. Artificial Neural Networks
✓ Deep learning relies on multiple layers of training.
✓ Artificial Neural Network is a computing system made up of a number of
simple, highly interconnected processing elements which process information
by their dynamic state response to external inputs.
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✓ It is an interconnected group of nodes akin to the vast network of layers of
neurons in a brain.
2.7.3. TensorFlow
❖ TensorFlow is the open source Deep Learning library provided by Google.
❖ It allows development of a variety of neural network applications such as computer
vision, speech processing, or text recognition.
❖ It uses data flow graphs for numerical computations.
3. Reason for choosing Machine Learning
➢ Learning machine learning brings in better career opportunities
✓ Machine learning is the shining star of the moment.
✓ Every industry looking to apply AI in their domain, studying machine learning
opens world of opportunities to develop cutting edge machine learning
applications in various verticals – such as cyber security, image recognition,
medicine, or face recognition.
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✓ Several machine learning companies on the verge of hiring skilled ML
engineers, it is becoming the brain behind business intelligence.
➢ Machine Learning Jobs on the rise
✓ The major hiring is happening in all top tech companies in search of those
special kind of people (machine learning engineers) who can build a hammer
(machine learning algorithms).
✓ The job market for machine learning engineers is not just hot but it’s sizzling.
✓ Machine Learning Jobs on Indeed.com - 2,500+(India) & 12,000+(US)
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Learning Outcome
➢ Have a good understanding of the fundamental issues and challenges of
machine learning: data, model selection, model complexity, etc.
➢ Have an understanding of the strengths and weaknesses of many popular
machine learning approaches.
➢ Appreciate the underlying mathematical relationships within and across
Machine Learning algorithms and the paradigms of supervised and unsupervised
learning. ➢ Be able to design and implement various machine learning algorithms in a
range of real-world applications.
➢ Ability to integrate machine learning libraries and mathematical and statistical
tools with modern technologies
➢ Ability to understand and apply scaling up machine learning techniques and
associated computing techniques and technologies.

A internship report on artificial intelligence

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