Department of Electronics and Communication Engineering

Institute of Engineering and Technology, Khandari Campus

(Dr. Bhim Rao Ambedkar University, Agra)

Industrial Training Report

on
MACHINE LEARNING

Submitted by:- Submitted by:-

SUMIT SAXENA Er. ANKITA MAHESHWARI
21ECE14 (ECE-COORDINATOR)

SESSION 2024-25
BATCH 2021-25
 DECLARATION
I hereby declare that I have completed my four weeks summer training at CODTECH IT
SOLUTIONS (one of the world’s leading online certification training providers) from 15TH JULY
2024, to 15TH AUGUST 2024. I have declared that I have worked with full dedication during these
four weeks of training and my learning outcomes fulfill the requirements of training for the
award of degree of Bachelor Of Engineering (B.E), INSTITUTE OF ENGINNERING &
TECHNOLOGY, AGRA

………………………………………

(Signature of Student)

Name of Student: - Subhadip Mondal

Registration No: -11701711

Date:…………………
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 ACKNOWLEDGEMENT
The success and final outcome of learning Machine Learning required a lot of guidance and
assistance from many people and I am extremely privileged to have got this all along the
completion of my course and few of the projects. All that I have done is only due to such
supervision and assistance and I would not forget to thank them.
I respect and thank CODTECH IT SOLUTIONS, for providing me an opportunity to do the course
and project work and giving me all support and guidance, which made me complete the course
duly.
I am thankful to and fortunate enough to get constant encouragement, support and guidance
from all Teaching staffs of CODTECH IT SOL which helped us in successfully completing my
course and project work.

………………………………………

(Signature of Student)

Name of Student: - Subhadip Mondal

Registration No: -11701711

Date:………………..

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 TABLE OF CONTENTS
1. Introduction……………………………………………………………………………………………………05
1.1. A Taste of Machine Learning………………………………………………………………………………..05
1.2. Relation to Data Mining………………………………………………………….………………………….05
1.3. Relation to Optimization………………………………………………………….…………………………06
1.4. Relation to Statistics…………………………………………………………................................................06
1.5. Future of Machine Learning………………………………………………………........................................06

2. Technology Learnt…………………………………………………………………………………………….06
2.1. Introduction to Artificial Intelligence and Machine Learning……………………………………………....06
2.1.1. Definition of Artificial Intelligence………………………………………………………………………..06
2.1.2. Definition of Machine Learning………………………………………………………………………...…07
2.1.3. Machine Learning Algorithms……………………………………………………………………………..08
2.1.4. Applications of Machine Learning………………………………………...............................................…09

2.2. Techniques of Machine Learning…………………………………………………………………………....10

2.2.1. Supervised Learning…………………………………………...…………………………………………..11
2.2.2. Unsupervised Learning……………………………………………...……………………………………..14
2.2.3. Semi- supervised Learning……………………………………………..………………………………….16
2.2.4. Reinforcement Learning………………………………………………………………………………..….17
2.2.5. Some Important Considerations in Machine Learning……………………………………………….........17

2.3. Data Preprocessing………………………………………………………….…………………………….....18

2.3.1. Data Preparation………………………………………………………….………………………………..18
2.3.2. Feature Engineering…………………………………………………….…………………………………19
2.3.3. Feature Scaling………………………………………………………………………………………….…20

2.4. Supervised learning……………………………………………………………….…………………………20

2.4.1.3. Polynomial Regression…………………………………………………………………………………..21
2.4.1.4. Decision Tree Regression………………………………………………………………………………..22
2.4.1.5. Random Forest Regression……………………………………………………………………………....22

2.5.2. Classification………………………………………………………………………………………………23
2.5.2.1. Linear Models……………………………………………………………………………………….…...23
2.5.2.1.1. Logistic Regression………………………………………………………………………………..…..23
2.5.2.1.2. Support Vector machines…………………………………………………………………………..…..39
..
2.6.2.2. Nonlinear Models………………………………………………………………………………….…….40
2.6.2.2.1. K-Nearest Neighbors (KNN)…………………………………………………………………………..40

2.7 My projects during the internship

2.8 BIBLIOGRAPHY

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 MY PROJECTS DURING THE SUMMER INTERNSHIP
1. TASK ONE: LINEAR REGRESSION ON HOUSING PRICES
Implement linear regression to predict housing prices based on features like square footage, number of
bedrooms, and location. Use a dataset like the Boston Housing dataset for training and evaluation.

2. TASK TWO: ANALYSIS ON MOVIE REVIEWS

Develop a sentiment analysis model to classify movie reviews as
positive or negative. Use a dataset like the IMDb Movie Reviews
dataset for training and testing
3. TASK THREE: CREDIT CARD FRAUD DETECTION
Develop a fraud detection model to identify fraudulent credit card
transactions. Use techniques like anomaly detection or supervised
learning with imbalanced data.

4. TASK FOUR: BMI TRACKER APP

Develop an app that calculates Body Mass Index (BMI) based on user input
for height and weight.
5. TASK FIVE: TEXT-TO-SPEECH CONVERSION APPLICATION
Develop a text-to-speech conversion application that allows users to input
text and generate corresponding audio output. The application should
support multiple languages and voices, providing users with options to
customize the speech synthesis according to their preferences.

6. TASK SIX: TEXT-TO-IMAGE GENERATION APPLICATION

Develop a text-to-image generation application that takes textual
descriptions as input and produces corresponding visual representations or
images. The application should leverage techniques from the field of
generative models
 BIBLIOGRAPHY
Books

 Hastie, Friedman, and Tibshirani, The Elements of Statistical Learning, 2001

 Bishop, Pattern Recognition and Machine Learning, 2006
 Ripley, Pattern Recognition and Neural Networks, 1996
 Duda, Hart, and Stork, Pattern Classification, 2nd Ed., 2002
 Tan, Steinbach, and Kumar, Introduction to Data Mining, Addison-Wesley, 2005.

Other machine learning courses

 Andrew Ng
 Max Welling

Data repositories

 UCI Machine Learning Repository

 Physionet
 MNIST Handwritten Digits
 Handwritten digits, faces, text in Matlab format
 Medical imaging
 Image registration

Background

 The Matrix Cookbook by Kaare Brandt Petersen and Michael Syskind Pedersen.
 Convex Optimization by Boyd and Vandenberghe

Matlab Software

 CVX convex program solver by Stephen Boyd

 YALMIP, a high-level Matlab interface to a variety of convex program solvers, such as
SeDuMi
 SeDuMi, for solving second order cone programs. Most if not all tractable convex programs
can be cast as such.
 LIBSVM, for support vector classification (including multiclass), regression, and one-class
classification (novelty detection).

Conferences/Publications

 NIPS
 ICML
 AISTATS