SWAMI KESHVANAND INSTITUTE OF TECHNOLOGY,
MANAGEMENT & GRAMOTHAN, JAIPUR
Industrial Training
Machine Learning & Deep Learning
Department of Electronics & Communication Engineering
Presented to:
Mr. Abhinandan Jain
(Assistant Professor SKIT Jaipur)
Mr. Neeraj Jain
(Assistant Professor SKIT Jaipur)
SKIT/ECE/IndustrialTraining-AyushJajodia
Presented By:
Ayush Kumar Jajodia
7ECB-G2
18ESKEC711
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TABLE OF CONTENTS
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Machine Learning
Python in ML
Applications
Algorithms for ML Models
Deep Learning
Neural Networks
Summary
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MACHINE LEARNING

Machine Learning is a branch of Artificial Intelligence
& Computer science which focuses on the use of data
and algorithms to imitate the way that humans learn.

It is programming computers to optimize performance
criterion using example data set.

ML can detect patterns in data to predict future data
or other outcomes of interest.
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WHY ML?

Huge amount of data

Need

Statistics
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APPLICATIONS

Speech Recognition used in mobile phones is
an example of ML

Object Recognition

Predictive Models

Credit Risk Modelling

Product Recommendation
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PYTHON IN ML

Open
source General purpose Language

Easy to understand.

Widely
developed Library ecosystem.

Used
to
perform
Complex
tasks without extensive coding.
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STEPS IN BUILDING ML MODEL

Problem Formulation

Data Tidying

Pre-Processing

Train-Test Split

Model Building

Validation & Model Accuracy

Prediction
SKIT/ECE/IndustrialTraining-AyushJajodia
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TYPES OF ML ALGORITHMS
1.Supervised ML Algorithms:

Algorithms that make predictions on given set of
samples.

It searches for patterns within the value labels
assigned to data points.

Ex. Linear Regression, Logistic Regression ,Decision
Tree, Support Vector Machine
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2.Unsupervised ML Algorithms:

There are no labels associated with data
points. These ML Algorithms organize the data
into a group of clusters to describe its
structure and make complex data look
simple and organized for analysis.

It is preferable as it is easy to get unlabelled
data in comparison to labelled data.

Ex. KNN,K-Means clustering ,Neural Networks
etc.
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ALGORITHMS

Linear Regression:
It is a linear approach to modelling the
relationship between a dependent variable
and one or more independent variables.
It is a statistical method that is used for
predictive analysis. Linear Regression makes
predictions for continuous/real or numeric
variables such as sales, salary, age, etc.
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2.Logistic Regression: It is used for predicting the categorical
dependent variable using a given set of independent
variables.
It gives the probabilistic values which lie
and 1.
Linear regression is used for solving regression
whereas logistic regression is used for
classification problems.
between 0
problem
solving the
It uses sigmoid function (Logistic Function) which is a
mathematical function used to map the predicted values to
probabilities.
SKIT/ECE/IndustrialTraining-AyushJajodia
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DIFFERENCE B/W LINEAR & LOGISTIC
REGRESSION

Equation in Linear Regression=> Y= Bo+B1X
whereas for logistic Regression eqn would be=>
p(X)=e^(Bo + B1X)/(1+ e^(Bo+B1X)).
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Decision Trees:
A decision tree is a decision support tool that uses
a tree like model of decisions and their possible
consequences ,including chance events like
outcomes, resource costs ,and utility.
It is one way to display an algorithm that only
contains conditional control statements.
Lacks a bit in accuracy.
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Support Vector Classifier : It is a supervised
ML algorithm that is capable of performing
classification,regression
and event
outlier
detection .The Linear SVM classifier works by
drawing a straight line between 2 classes.
The algorithm creates a line or a hyperplane
which separates the data into classes.
It uses kernels to create non-linear boundaries.
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Deep Learning
Deep learning is a type of machine learning
and AI that imitates the way human gain
certain type of knowledge.
It is way to automate predictive analysis.
While traditional ML algorithms are linear, deep
learning algorithms are stacked in a hierarchy of
increasing complexity.
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NEURAL NETWORKS
Neural Networks make up the backbone of deep
learning algorithms.
It reflects the behaviour of the human brain,
allowing computer to recognize patterns and solve
common problems in the field of Artificial
Intelligence , Machine Learning & Deep Learning.
Types of Neural Networks:- (1) ANN,(2)CNN,(3)RNN
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ANN (Artificial Neural Networks)
A single neuron can be considered as a Logistic
Regression,and ANN is a group of multiple neurons
at each layer , that is Input, Hidden ,Output.
It is based on a collection of connected units or
nodes called artificial neurons, which loosely model
the neurons in a biological brain.
Once the ANN is trained, it can be used to predict
the outcome of another new set of input data.
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CNN (Convolutional Neural Network)
It is Deep Learning algorithm that can take an input
image and be able to differentiate one from
another.
This neural network model uses a variation of
multilayer neurons and contains one or more
convolutional layers that can be entirely connected.
It is used in image recognition and processing that is
specifically designed to process pixel data.
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TIME SERIES ANALYSIS
A time series is simply a series of data points with
time order this means we pick one or more
variables and note their values over time.
Time series forecasting is the use of a model to
predict future values based on previously
observed values.
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PROJECT 1.
Geospatial use: Determining with the help of
data sets whether Global Warming exists or not
with the help of graphical analysis.
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SUMMARY
In this presentatiin we understood the meaning
of machine learning and its importance.
Different Machine Learning Algorithms and how
these are used to build a model to predict
dependent variable such as price etc.
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THANK YOU !!
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