SEMESTER INTERNSHIP

ANDHRA PRADESH
STATE COUNCIL OF HIGHER EDUCATION
(A SATUTORY BODY OF GOVERNMENT OF ANDHRA PRADESH )
 PROGRAM BOOK FOR

SEMESTER INTERNSHIP

NAME OF THE STUDENT: B.Srimanth

NAME OF THE COLLEGE: Lingayas Institute of Management and Technology

REGISTRATION NUMBER: 20NA1A0596

PERIOD OF INTERNSHIP: 6 months FROM: 31 Jan 2024 TO: 30 Apr 2024

NAME OF THE INTERN ORGANIZATION: International Institute of Digital Technologies

ADDRESS OF THE INTERN ORGANIZATION: New Airport Road. Renigunta Tirupati.

Andhra Pradesh -517 520

Jawaharlal Nehru Technological University Kakinada

2024
 An Internship Report on
Chatbot: Natural Language Processing for Building Conversational Agents

Submitted in accordance with requirement for the degree of

Bachelor of Technology

Under the Faculty Guideship of

Mr. Ch. Srinivas Rao

Professor

Department of

Computer Science and Engineering

Lingayas institute of management and technology

Submitted by:

B.Srimanth

Regd. No: 20NA1A0596

Department of

Computer Science and Engineering

Lingayas Institute of Management and Technology

LINGAYAS INSTITUTE OF MANAGEMENT AND TECHNOLOGY

(Approved by AICTE, New Delhi & Affiliated to JNTU Kakinada)
Viveka Nagar, Madalavarigudem via Nunna, Vijayawada.
2023 – 2024
 Student’s Declaration

I, B.SRIMANTH a student of Cloud computing Program, Reg. No.

20NA1A0596 of the Department of Computer Science and Engineering,
Lingayas institute of management and technology do hereby declare that I have
completed the mandatory internship from to in Edunet Foundation Under the
Faculty Guideship of Mr. Ch. Srinivas Rao, Department of Computer Science and
Engineering, Lingayas Institute of Management and Technology.

(Signature and date)

 Official Certification

This is to certify that B. Srimanth Reg. No. 20NA1A0596 has completed his
internship in IIDT under my supervision as a part of partial fulfilment of the
requirement for the degree of Bachelor of Technology in the Department of
Computer Science and Engineering, Lingayas Institute of Management and
Technology.

(Signature with date and seal)

Endorsements

Faculty Guide

Head of the Department

Principal
Certificate From Intern Organization
 Acknowledgements

I take this opportunity to thank all who have rendered their full support to
our work. The pleasure, the achievement, the glory, the satisfaction, the reward,
the appreciation and the construction of my internship cannot be expressed with
a few words for their valuable suggestions.

I would like to express my gratitude to our beloved Chairman, Mr.

GADDE RAJALING sir for his great motivation and encouragement towards
internship.

I am thankful to the Principal, Dr. N. Satyanarayana sir for his

encouragement and facilities provided during the course of the internship.

I am great full to Dr B Satyanarayana Sir, R&C for his encouragement

in completing the internship program within the time

Finally, I thank the Administrative Officer, Staff Members, Faculty of

Department of CSE, Lingayas Institute of Management and Technology and our
friends, who directly or indirectly helped us in the completion of this internship.

With Profound Gratitude

B.SRIMANTH

20NA1A0596
 Contents

S. No. Topics Page No.

1. Executive summary

2. Overview of the organization

3. Internship Part

4. Activity Log and Weekly Reports

5. Outcomes Description

5.1 Describe the work environment experienced

5.2 Real-time technical skills I have acquired

5.3 Managerial skills I have acquired

5.4 I have improved my communication skills

5.5 Enhancing my abilities in group discussion,
participation in teams, contribution as a team
member

5.6 Technology developments I have observed

6. Student evaluation in short-term internship

7. Evaluation of the supervisor of the intern’s

organization

8. Photos and video links

9. Internal evaluation for short-term internship

9.1 Objectives

9.2 Assessment model

10. Internal assessment statement

11. External assessment statement

 Chapter 1: Executive summary
During the six-month virtual internship at the International Institute of Digital Technologies
(IIDT), I worked on the project "Chatbot: Natural Language Processing Question Answering
for Building Conversational Agents." This internship provided a comprehensive learning
experience in the field of Artificial Intelligence and Machine Learning (AI-ML), focusing on
developing advanced conversational agents using Natural Language Processing (NLP)
techniques.

Learning Objectives and Outcomes Achieved:

1. Gain proficiency in NLP algorithms and techniques for chatbot development.

2. Develop a deep understanding of AI-ML concepts and their applications in building
conversational agents.
3. Acquire hands-on experience in programming languages such as Python and
frameworks like TensorFlow and PyTorch.
4. Enhance problem-solving skills through real-world AI challenges and project
development.
5. Improve communication and collaboration skills through team-based projects and
interactions with industry professionals.

Throughout the internship, I achieved these learning objectives by actively participating in

classes held every alternate day, completing assignments, and engaging in daily tests to
reinforce knowledge. Additionally, the monthly employability test provided a platform to
assess and enhance practical skills essential for AI-ML roles in the industry.

The internship was conducted in the dynamic realm of AI-ML, focusing on developing cutting-
edge technologies for conversational AI. IIDT, as a leading provider of digital technologies
education, offered a structured and immersive learning environment, combining theoretical
knowledge with hands-on projects to prepare interns for AI-ML roles in the industry.

During the internship period, I engaged in a variety of activities, including:

1. Attending virtual classes on AI-ML concepts, NLP techniques, and chatbot

development strategies.
2. Participating in hands-on coding sessions to implement NLP algorithms and develop
chatbot prototypes.

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 3. Collaborating with team members on project assignments to solve real-world AI
challenges.
4. Completing daily tests to assess understanding and retention of AI-ML concepts.
5. Preparing for and taking the monthly employability test to evaluate practical skills and
readiness for AI-ML roles.

Overall, this internship provided a valuable learning experience, equipping me with the skills
and knowledge necessary to excel in the field of AI-ML and contribute effectively to the
development of innovative conversational AI solutions.

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 Chapter 2: Overview of the organization

The International Institute of Digital Technologies (IIDT) stands as a beacon of excellence in

the realm of digital technology education, boasting a rich history of empowering individuals
with cutting-edge skills and knowledge crucial for success in the digital era.

IIDT's overarching vision is to emerge as a preeminent global leader in digital technology

education, driving forward innovation and setting new benchmarks of excellence in areas such
as AI-ML, cybersecurity, data science, and other emerging fields crucial to the digital
landscape. Anchored by a robust mission, IIDT is committed to equipping individuals with not
just theoretical knowledge but also practical skills, industry insights, and a growth mindset
essential for navigating the complexities of modern digital environments and making
meaningful contributions to the digital transformation of businesses and society at large.

The organizational culture at IIDT is deeply rooted in core values that guide its every action
and decision. Integrity, collaboration, innovation, and a commitment to lifelong learning are
not just words but principles that permeate through every facet of IIDT's operations, ensuring
a student-centric approach and a relentless pursuit of excellence.

In terms of policies pertaining to intern roles, IIDT prioritizes a hands-on learning experience,
providing interns with immersive opportunities to work on real-world projects, engage in
mentorship programs with seasoned professionals, and leverage state-of-the-art tools and
technologies. Interns are encouraged to actively participate in class discussions, collaborate on
industry-relevant projects, and seek guidance and feedback from experienced faculty members
and industry experts, thus fostering a culture of continuous growth and development.

The organizational structure at IIDT is meticulously designed to foster collaboration and

innovation across various departments, including AI-ML, cybersecurity, data science, and
digital marketing. This collaborative approach ensures a holistic and comprehensive learning
experience for students and interns alike, enabling them to gain a deep understanding of the
interconnectedness of digital technologies and their applications in real-world scenarios.

Within the AI-ML department, where the intern is placed, employees assume diverse roles and
responsibilities, ranging from conducting groundbreaking research on AI algorithms to
developing and deploying sophisticated AI models, implementing advanced NLP techniques

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for chatbot development, rigorously testing and optimizing AI systems, and staying abreast of
the latest advancements in the ever-evolving field of AI-ML.

In terms of performance metrics, IIDT has consistently demonstrated remarkable growth and
impact within the digital education sector, achieving commendable turnover, profits, market
reach, and market value. This success is attributed to IIDT's unwavering commitment to
delivering quality education, forging strategic partnerships with industry leaders, and
prioritizing student success and employability.

Looking towards the future, IIDT is poised for further expansion and innovation. The
organization envisions launching new programs in emerging technologies, strengthening
collaborative efforts with industry stakeholders, enhancing student support services, and
continuing to lead the charge in digital technology education on a global scale.

In conclusion, IIDT offers interns a dynamic and enriching environment characterized by

valuable learning opportunities, mentorship from industry veterans, exposure to cutting-edge
technologies, and a culture of innovation and excellence that propels individuals towards
success in the dynamic digital landscape.

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 Chapter 3: Internship part

During the six-month virtual internship at the International Institute of Digital Technologies
(IIDT) in the AI-ML department, I was immersed in a range of activities and responsibilities
that provided a comprehensive learning experience in the development of chatbots using
Natural Language Processing (NLP) techniques.

The working conditions during the internship were highly conducive to learning and
collaboration. Despite the virtual setting, the internship program was structured to ensure
regular communication and interaction with supervisors, mentors, and fellow interns. Weekly
meetings and check-ins facilitated ongoing feedback and guidance, creating a supportive
environment for professional growth.

The weekly work schedule at IIDT included a mix of virtual classes, project work, and
independent study. Classes were held every alternate day, covering topics such as AI
algorithms, NLP fundamentals, chatbot development strategies, and industry best practices.
These sessions were complemented by hands-on projects and assignments designed to apply
theoretical knowledge to practical scenarios.

In terms of equipment used, interns were provided access to essential tools and resources for
AI-ML development. This included access to cloud computing platforms for model training
and testing, programming environments such as Python and Jupyter Notebooks, and
collaboration tools for team projects and communication.

The tasks performed during the internship spanned various aspects of chatbot development.
This included:

• Researching and implementing NLP algorithms for text processing and understanding.
• Designing conversational flows and dialogue management systems for chatbots.
• Integrating AI models into chatbot frameworks for question answering and task
completion.
• Testing and evaluating chatbot performance through user simulations and feedback
loops.
• Collaborating with team members on project milestones, code reviews, and
documentation.

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Reflecting on the internship experience, I acquired a diverse set of skills that are invaluable in
the field of AI-ML and chatbot development. These include proficiency in NLP techniques,
hands-on experience with AI frameworks and tools, project management skills through
collaborative work, and the ability to adapt to dynamic and fast-paced work environments. The
internship not only deepened my technical expertise but also enhanced my problem-solving
abilities, communication skills, and overall readiness for AI-ML roles in the industry.

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 Activity Log for the First Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Foundational
Day-1
Python Prerequisite course understanding and pro
(31-01-24)
ciency in theconcepts

Foundational
Day-2
Python Prerequisite course understanding and pro
(01-02-24)
ciency in theconcepts

Foundational
Day-3
Python Prerequisite course understanding and pro
(02-02-24)
ciency in theconcepts

Foundational
Day-4 Machine Learning
understanding and pro
(03-02-24) Prerequisite course
ciency in theconcepts

Foundational
Day-5 Machine Learning
understanding and pro
(04-02-24) Prerequisite course
ciency in theconcepts

Foundational
Day-6 Machine Learning
Prerequisite course understanding and pro
(06-02-24) ,Profiling Test ciency in theconcepts

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 Weekly Report

Week – 1 (From: 31-01-24 To: 06-02-24 )

Objective of the Activity Done:

1. Python Prerequisite Course: The objective of these activities was to establish a solid
foundation in Python programming, covering essential concepts such as data types,
loops, functions, and libraries. By completing this course, I aimed to gain proficiency
in Python, a crucial skill for AI and ML development.
2. Machine Learning Prerequisite Course: The focus of these activities was to
introduce fundamental Machine Learning concepts, including algorithms, data
preprocessing, model training, and evaluation. Through hands-on exercises and
theoretical learning, I aimed to understand the principles of Machine Learning and its
applications in AI projects.

Detailed Report:

During Week 1 of the internship, I engaged in intensive learning sessions focused on Python
programming and Machine Learning fundamentals. The Python Prerequisite course spanned
the first three days, where I covered topics such as data structures, control flow, functions, and
libraries like NumPy and Pandas. These sessions provided a strong foundational understanding
of Python, enabling me to write efficient code and manipulate data effectively.

Transitioning into Machine Learning on Day 4, I delved into concepts like supervised and
unsupervised learning, regression, classification, and clustering algorithms. Practical examples
and exercises helped solidify my understanding of how Machine Learning algorithms work
and their applications in real-world scenarios.

By the end of the week, I completed the Machine Learning Prerequisite course and took a
profiling test to assess my knowledge and skills. The test evaluated my ability to apply Machine
Learning concepts to solve problems and demonstrated my proficiency in foundational
Machine Learning techniques.

8|Page
 Activity Log for the Second Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Participants will explore

fundamental concepts of
data types and basic data
Day-1 Session-1- Data Types,Basic
structures essential for
(07-02-24) Data Structure
building astrong foundation
in programming.

An assessment which
Day-2
Daily test-1 covers the previous session
(08-02-24)
topics

The participate will get

outcomes such as
mastering
user-defined and built-in
Session-2Functions, Inbuilt functions, understanding
Day-3 Functions, Modules-os, sys, moduleimports (os, sys,
(09-02-24) inter tools, Collections,Math itertools, collections, math),
and utilizing them
effectively in Python
programming.

An assessment which
Day-4
Daily test-2 covers the previous session
(10-02-24)
topics

Loops and flow control

statements dictate the
Session3-Loops & Flow
Day-5 sequence and repetition of
ControlStatements
(11-02-24) actions within a program,
enabling efficient execution

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 of code based on specified
conditions.

An assessment which
Day-6
Daily test-3 covers the previous session
(12-02-24)
topics

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 Weekly Report

Week – 2 (From: 07-02-24 To: 12-02-24)

Objective of the Activity Done:

1. Data Types, Basic Data Structure: The objective was to introduce participants to
fundamental concepts like data types and basic data structures, providing a strong
foundation for programming.
2. Functions, Inbuilt Functions, Modules: The objective was for participants to master
user-defined and built-in functions, understand module imports (os, sys, itertools,
collections, math), and effectively use them in Python programming.
3. Loops & Flow Control Statements: The objective was for participants to understand
how loops and flow control statements work, allowing efficient code execution based
on conditions.

Detailed Report:

Week 2 of the internship focused on essential Python programming concepts crucial for the
Chatbot project's development. The sessions covered in-depth topics such as data types, basic
data structures, functions, inbuilt functions, modules, loops, and flow control statements.

Participants actively engaged in hands-on learning activities, including coding exercises,

discussions, and daily tests to assess comprehension. The daily tests served as checkpoints to
reinforce learning and track progress throughout the week.

By the end of Week 2, participants had gained a solid understanding of Python fundamentals,
including data handling, function usage, module imports, and control flow mechanisms. These
skills laid a robust foundation for diving deeper into Natural Language Processing and building
conversational agents in the upcoming weeks of the internship.

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 Activity Log for the Third Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Covering File Handling,

Pickle Files, Exception
Handling, and Regex
Session-4File Handling,
introduces essential Python
Day-1 Pickle Files Introduction,
functionalities formanaging
(13-02-24) Exception Handling, Regex
files, serializing data,
handling errors, and pattern
matching.
An assessment which
covers the previous session
Day-2 Daily Test-4,Python topics, Foundational
(14-02-24) Prerequisite course understanding and
proficiency in the concepts
Object-Oriented
Programming (OOP)
facilitates structured code
organization and design in
Day-3
Session5-Oops, Numpy Python, while NumPy
(15-02-24)
provides powerful array
manipulation and numerical
computing capabilities.

An assessment which
Day-4
Daily Test-5 covers the previous session
(16-02-24)
topics

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 Utilizing Pandas for data
manipulation, conducting
Exploratory Data Analysis
Session6-Pandas, EDA, Web (EDA), and employing web
Day-5 Scrapping-Selenium/ requests
scraping techniques with
(17-02-24) & beautifulsoup
Selenium/Requests and
Beautiful Soup to extract
data from websites.
An assessment which
covers the previous session
Daily Test-6,Machine Learning
Day-6 topics and Foundational
Prerequisite course
(19-02-24) understanding and
proficiency in the concepts

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 Weekly Report

Week – 3 (From: 13-02-24 To: 19-02-24)

Objective of the Activity Done:

1. File Handling, Pickle Files, Exception Handling, Regex: The objective was to
introduce essential Python functionalities for managing files, serializing data, handling
errors, and pattern matching, crucial for data processing and analysis in the Chatbot
project.
2. Daily Tests: These assessments aimed to gauge comprehension of previous session
topics, reinforcing foundational Python and Machine Learning concepts essential for
the project's development.
3. OOP, NumPy: Understanding Object-Oriented Programming principles and gaining
proficiency in NumPy were key objectives to enhance code organization, data
manipulation, and numerical computing skills.
4. Pandas, EDA, Web Scraping: The goal was to utilize Pandas for efficient data
manipulation, conduct Exploratory Data Analysis (EDA) to understand data patterns,
and learn web scraping techniques for data extraction from websites, essential for
gathering training data for the Chatbot.

Detailed Report:

Week 3 of the internship was focused on deepening Python programming skills, exploring
Object-Oriented Programming (OOP) principles, mastering data manipulation with Pandas,
conducting Exploratory Data Analysis (EDA), and learning web scraping techniques.

Sessions on File Handling, Pickle Files, Exception Handling, and Regex provided a strong
foundation in handling data, managing files, and implementing error handling strategies. Daily
tests ensured continuous assessment and reinforcement of Python concepts.

The OOP and NumPy sessions expanded my understanding of structured code organization,
design patterns, and efficient array manipulation for numerical computations, laying a solid
groundwork for data processing tasks in the Chatbot project.

Exploring Pandas for data manipulation, conducting EDA to glean insights from data, and
learning web scraping techniques using Selenium/Requests and Beautiful Soup were pivotal in

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acquiring data extraction skills essential for building a robust training dataset for the Chatbot's
Natural Language Processing (NLP) capabilities.

The weekly assessments not only evaluated my progress but also highlighted areas for further
improvement and learning. Overall, Week 2 was instrumental in strengthening technical skills
and knowledge crucial for the successful development of the Chatbot project.

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 Activity Log for the Fourth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Statistics encompasses
descriptive analysis,
Session 7- Statistics - probability theory, various
Descriptive, Probability, distributions, inferential
Distributions, Inferential methods like Central Limit
Statistics - CLT, CI, Theorem (CLT),
Hypothesis Testing, Confidence Intervals (CI),
Day-1 critical regions, level of Hypothesis Testing with
(20-02-24) significance, errortypes, critical regions and
Choosing P-values from z,t,f- significancelevels, error
tablesand how to use them for types, and utilizing P-
feature selection values from tables for
feature selection in
predictive modeling.

An assessment which
Day-2
Daily Test-7 covers the previous session
(21-02-24)
topics

To connect a SQL database

to Python, using libraries
Session-8- SQL, How to such as SQLAlchemy or
Day-3 connect SQLdatabase to psycopg2 for PostgreSQL,
(22-02-24) Python pyodbc for SQL Server, or
pymysql for MySQL in a
Python script.

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 An assessment which
Day-4
Daily Test-8 covers the previous session
(23-02-24)
topics

Machine learning basics

encompass understanding
algorithms, preprocessing
Session-9-Machine learning - data through feature
Basics, Feature Engineering- engineering, including
Categorical Encodingand categorical encoding and

Day-5 Feature Scaling,Feature feature scaling, performing

(24-02-24) transformation& Selection, feature transformation and

loss / Cost functions, selection, utilizing loss or
Evaluation Metric cost functions for
optimization, and assessing
model performance using
evaluation metrics.
An assessment which
covers the previous session
Day-6 Daily Test-9,Python topics and Foundational
(26-02-24) Prerequisite course understanding and
proficiency in the concepts

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 Weekly Report

Week – 4 (From: 20-02-24 To: 26-02-24)

Objective of the Activity Done:

1. Session 7- Statistics: The objective was to cover statistics fundamentals including

descriptive analysis, probability theory, various distributions, inferential methods like
Central Limit Theorem (CLT), Confidence Intervals (CI), Hypothesis Testing, critical
regions, significance levels, error types, and using P-values for feature selection in
predictive modeling.
2. Session 8- SQL and Database Connectivity: The focus was on learning how to
connect SQL databases to Python scripts using libraries such as SQLAlchemy,
psycopg2, pyodbc, or pymysql, enhancing skills in data manipulation and integration.
3. Session 9- Machine Learning Basics: Participants delved into machine learning basics
such as algorithm understanding, feature engineering techniques like categorical
encoding and feature scaling, feature transformation and selection, utilization of loss or
cost functions for model optimization, and evaluation metrics for assessing model
performance.

Detailed Report:

Week 4 of the internship was dedicated to advancing skills in statistics, SQL, database
connectivity, and machine learning basics. Each day's activities focused on practical
demonstrations, assessments, and learning sessions to enhance understanding and proficiency
in these areas.

Day 1 covered a comprehensive session on statistics, including descriptive analysis, probability

theory, distributions, inferential methods like CLT and CI, hypothesis testing, and utilizing P-
values for feature selection in predictive modeling. Day 2 involved an assessment to evaluate
comprehension of these statistics concepts.

On Day 3, participants learned how to connect SQL databases to Python scripts, expanding
their data manipulation and integration capabilities. Day 4 included an assessment to gauge
understanding of SQL and database connectivity topics.

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Day 5 introduced machine learning basics, covering algorithm understanding, feature
engineering techniques like categorical encoding and feature scaling, feature transformation
and selection methods, and the use of loss functions and evaluation metrics for optimizing and
assessing model performance. Day 6 concluded with tests assessing understanding of machine
learning concepts and foundational Python skills.

Overall, Week 4 provided a solid foundation in statistics, SQL, database connectivity, and
machine learning basics, essential for progressing towards more complex AI-ML tasks and
projects like the Chatbot: Natural Language Processing Question Answering for Building
Conversational Agents project.

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 Activity Log for the Fifth Week

Person In-
Brief description of the
Day / Hrs Learning Outcome Charge
daily activity
Siignature

Daily 10-Classification &

Regression
Demonstration of implementing
Algorithms &
classification and regression
hyperparameters, Sklearn
Day-1 algorithms with hyperparameter
library useful functions
(27-02-24) tuning using Sklearn library's
implementation
useful functions.
demonstration

Day-2 An assessment which covers

Daily Test-10
(28-02-24) the previous session topics

Daily Test-11-Overfitting
& Underfittinghandling,
hyperparameter tuning,
How tochoose an
algorithm based on data,
Unsuperviesd Learning -
Building pipelines involves
Clustering algos&
sequentially integrating
Dendrograms,
preprocessing, feature selection,
Association,
algorithm selection, and
Day-3 Dimesionality
evaluation steps to optimize
(29-02-24) reduction-PCA,t-
model performance and ensure
SNE,Reinforcement
efficient data processing and
learning &
learning.
Semi-supervised learning
Introduction.Pipelines
building

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Day-4 An assessment which covers
Daily Test-11
(01-03-24) the previous session topics

Session-12-Deep
Learning Introduction -
Activation functions,
Participants will learn well
Day-5 Optimizers, Loss/Cost
about activation functions and
(02-03-24) functions, Perceptron,
optimizers, loss and cost
MLP,ANN
functions,perceptron,MLP,ANN
Architecture, Batch
architecture,batch size, epoch
size,Epoch,
,learning rate ,batch
Learning rate, Batch
normalization,dropout ,
Normalization, dropout,
parameters and hyper
Parameters and
parameters
hyperparameters
An assessment which covers
Daily Test-12 ,February
Day-6 the previous session topics and
Employability Test,Python
(04-03-24) Foundational understanding
Prerequisite course
and proficiency in the concepts

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 Weekly Report

Week – 5 (From: 27-02-24 To: 04-03-24)

Objective of the Activity Done:

1. Daily 10-Classification & Regression Algorithms: The objective was to demonstrate

implementing classification and regression algorithms with hyperparameter tuning
using Sklearn library functions, enhancing practical skills in model building and
evaluation.
2. Daily Test-10: This test aimed to assess understanding and retention of classification,
regression, and hyperparameter tuning concepts covered in previous sessions.
3. Daily Test-11: The focus was on assessing knowledge of overfitting and underfitting
handling, algorithm selection based on data, unsupervised learning techniques, and
building pipelines for efficient data processing.
4. Session-12-Deep Learning Introduction: Participants learned about deep learning
concepts such as activation functions, optimizers, loss functions, neural network
architectures, and training parameters, preparing them for advanced AI-ML topics.

Detailed Report:

Week 5 was dedicated to advancing skills in classification, regression, hyperparameter tuning,

unsupervised learning, and deep learning concepts. The daily activities included practical
demonstrations, assessments, and deep learning introductions to solidify knowledge and skills.

Day 1 focused on implementing classification and regression algorithms with hyperparameter

tuning using the Sklearn library, enhancing proficiency in model development and evaluation.
Day 2 involved a test to evaluate understanding of the previous session's topics, ensuring
retention and application of concepts.

On Day 3, participants learned about handling overfitting and underfitting, hyperparameter

tuning, algorithm selection based on data characteristics, unsupervised learning techniques like
clustering and dimensionality reduction, and building pipelines for efficient data processing.
Day 4 included an assessment to gauge comprehension of these advanced topics.

Day 5 introduced deep learning concepts, covering activation functions, optimizers, loss
functions, neural network architectures, and training parameters. This session prepared

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participants for delving deeper into artificial neural networks and deep learning models. Day 6
concluded with tests assessing understanding of deep learning concepts, employability skills,
and foundational Python proficiency.

Overall, Week 5 was pivotal in expanding knowledge and skills in AI-ML, preparing
participants for more complex tasks and projects like the Chatbot: Natural Language
Processing Question Answering for Building Conversational Agents project.

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 Activity Log for the Sixth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Comprehensive
understanding of neural
Session-13-Forward and network training processes,
Backward Propagation, including forward and
Vanishing and Exploding backward propagation,
Gradient how to handle handling overfitting and
Day-1
overfitting andunderfitting, underfitting through
(05-03-24)
hyperparameter tuning, regularization techniques,
regularization techniques, and hyperparameter tuning
for optimal model
performance

An assessment which
Day-2
Daily Test13 covers the previous session
(06-03-24)
topics

Session-14-How to use
Github, Git & Streamlit /
Flask. Pytorch,
Tensorflow, Comprehensive
Keras, keras_ocr,for understanding of version
architecture building, control with Git/GitHub,
preprocessing and required utilizing various deep
functionality. ANN basic learning frameworks for
model building and architecture building and
Day-3
selecting better-performing preprocessing,
(07-03-24)
models and model implementing basic
saving(h5 or pickle). Colab artificial neural network
file introduction and models, handling large data

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 different ways to handle files, and interpreting loss
large data files, importing and accuracy curves for
data files fromkaggle, model evaluation.
google drive etc. Loss and
accuracy curves interpretation.

An assessment which
Day-4
Daily Test-14 covers the previous session
(08-03-24)
topics

Session15-CNN
introduction-layers, image
data basics-RGB, B/W,
pixels, digitalization,
sampling & quantization, Comprehensive
filtering, Different libraries to understanding of
Day-5 handle/process image data, convolutional neural
(09-03-24) preprocessing the image files networks (CNNs), image
like resizing, color data fundamentals,
adjustments, blurring, noise preprocessing techniques
removals, etcand its including resizing, color
implementation functions in adjustments, and noise
libraries, convolution- removal, and strategies for
backward, opencv library handling overfitting and
functions for preprocessing, underfitting in image
Overfitting & Underfitting classification tasks.
handling
Daily Test-15, Machine An assessment which
Day-6 Learning Prerequisite course covers the previous session
(11-03-24) topics and Foundational
understanding and
proficiency in the concepts

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 Weekly Report
Week – 6 (From: 05-03-24 To: 11-03-24 )

Objective of the Activity Done:

1. Neural Network Training: The objective was to understand neural network training
processes, including forward and backward propagation, handling overfitting and
underfitting, hyperparameter tuning, and regularization techniques for optimal model
performance.
2. Version Control and Deep Learning Frameworks: Participants learned about version
control with Git/GitHub, utilizing deep learning frameworks like Pytorch, Tensorflow,
and Keras for architecture building, preprocessing, and model saving, along with
handling large data files and interpreting model performance metrics.
3. Convolutional Neural Networks (CNNs) and Image Data Processing: This session
aimed to provide a comprehensive understanding of CNNs, image data basics including
RGB, grayscale, pixels, preprocessing techniques like resizing and noise removal, and
strategies for handling overfitting and underfitting in image classification tasks.

Detailed Report:

Week 6 focused on advanced topics in neural networks, deep learning frameworks, and image
data processing, crucial for the Chatbot: Natural Language Processing Question Answering for
Building Conversational Agents project.

Day 1 delved into neural network training processes, covering forward and backward
propagation, handling overfitting and underfitting, hyperparameter tuning, and regularization
techniques. Day 2 involved an assessment to evaluate comprehension of these complex topics.

Day 3 introduced version control with Git/GitHub, deep learning frameworks like Pytorch,
Tensorflow, and Keras for architecture building, basic ANN model construction, handling large
data files, and interpreting model performance metrics. Day 4 included an assessment to gauge
understanding of these advanced concepts.

On Day 5, participants explored CNNs, image data basics, preprocessing techniques, and
strategies for handling overfitting and underfitting in image classification tasks. Day 6

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concluded with tests assessing understanding of deep learning concepts and foundational
machine learning skills.

Overall, Week 6 provided participants with a comprehensive understanding of advanced neural

network training, deep learning frameworks, and image data processing techniques, essential
for developing sophisticated AI models like the Chatbot project.

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 Activity Log for the Seventh Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Session -16 CNN pre-

Thorough comprehension
trained models and
of CNN pre-trained models,
architecture, transfer
transfer learning
learning and case
Day-1 techniques, building CNN
study, how to build CNN
(12-03-24) architectures with
architecture using tensorflow
TensorFlow and Keras, and
and keras, how to handle
strategies for addressing
overfitting & underfitting
overfitting and underfitting
issues
challenges.

An assessment which
Day-2 Daily Test-16 covers the previous session
topics

Thorough understanding of
Session-17-regularization regularization techniques,
techniques, and evaluation evaluation metrics,
Day-3 metrics. How to choose and selection and utilization of
usepre-trained models. optical pre-trained models, and
character recognition practical implementation of
optical characterrecognition
techniques.

An assessment which
Day-4 Daily Test-17 covers the previous session
topics

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 Session-18 Image
classification,
segmentation. Video
Classification. Object
Detection, Bounding Box
Regressor, IOU,
Yolo & SSD Comprehensive
explanation, understanding of
Day-5 semantic regularization techniques,
segmentation,U-net, evaluation metrics,
Inception and selection and utilization of
MobileNet models, pre-trained models, and
variants of optical character
convolution, the Siamese recognition methods.
network for metric learning.
fastai library, r-cnn, fastr-cnn,
faster r-cnn
An assessment which
covers the previous session
Day-6 Daily Test-18,Python topics and Foundational
Prerequisite course understanding and
proficiency in the concepts

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 Weekly Report

Week – 7 (From: To: )

Objective of the Activity Done:

1. CNN Pre-trained Models and Architecture: The objective was to understand CNN
pre-trained models, transfer learning techniques, building CNN architectures with
TensorFlow and Keras, and strategies for addressing overfitting and underfitting
challenges in deep learning models.
2. Regularization Techniques and Evaluation Metrics: Participants learned about
regularization techniques, evaluation metrics, selection and utilization of pre-trained
models, and practical implementation of optical character recognition techniques,
enhancing their skills in model optimization and performance evaluation.
3. Image Classification, Segmentation, and Object Detection: This session aimed to
provide a comprehensive understanding of image classification, segmentation
techniques, video classification, object detection methods, and various convolutional
network models like U-net, Inception, MobileNet, and variants for different tasks,
expanding knowledge in computer vision applications.

Detailed Report:

Week 7 focused on advanced topics in deep learning and computer vision, essential for the
Chatbot: Natural Language Processing Question Answering for Building Conversational
Agents project.

Day 1 covered CNN pre-trained models, transfer learning, building CNN architectures with
TensorFlow and Keras, and strategies for handling overfitting and underfitting challenges in
deep learning models. Day 2 involved an assessment to evaluate comprehension of these
complex topics.

On Day 3, participants learned about regularization techniques, evaluation metrics, selection

and utilization of pre-trained models, and practical implementation of optical character
recognition techniques, enhancing their skills in model optimization and performance
evaluation. Day 4 included an assessment to gauge understanding of these advanced concepts.

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Day 5 delved into image classification, segmentation techniques, video classification, object
detection methods, and various convolutional network models like U-net, Inception,
MobileNet, and variants, providing a comprehensive understanding of computer vision
applications. Day 6 concluded with tests assessing understanding of advanced topics and
foundational Python skills.

Overall, Week 7 provided participants with a deep understanding of advanced deep learning
and computer vision techniques, crucial for developing sophisticated AI models like the
Chatbot project.

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 Activity Log for the Eighth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

In-depth exploration of
Natural Language
Session -19-NLP basics-
Processing (NLP) basics
tokenization, stopwords,
including tokenization, stop
Day-1 normalization, stemming &
words, normalization,
Lemmatization,preprocessing
stemming, lemmatization,
in NLP Bagof words,BOW
and preprocessing
techniques such as Bag of
Words (BOW) model.

An assessment which
Day-2 Daily Test-19 covers the previous session
topics

Session 20-TF+IDF as In-depth exploration of TF-

features, n-grammodel and IDF as features, n-gram and
channel model, nltk library channel models, practical
Day-3
useful functions and implementation of NLTK
implemtation , recommender libraryfunctions, and an
systems, overview of recommender
systems.

An assessment which
Day-4 Daily Test-20 covers the previous session
topics

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 Session -21-Word
embeddings- word2vec,
glove, POS tagger, NER,
Comprehensive
RNN, LSTM,
understanding of TF-IDF
LLM Models and
as features,
Transformers, text
Day-5 n-gram and channel
analysis-semantic
models, practical
analysis, sentimental
implementation of NLTK
analysis, spacy library,
library functions, and an
Time Series
overview of recommender
Analysis- ARIMA, SARIMA,
systems.
Prophet &MLOPs basics
An assessment which
covers the previous session
Daily Test-21,Machine
Day-6 topics and Foundational
Learning Prerequisite course
understanding and
proficiency in the concepts

33 | P a g e
 Weekly Report

Week – 8 (From: To: )

Objective of the Activity Done:

1. NLP Basics: The objective was to delve into NLP basics including tokenization, stop
words, normalization, stemming, lemmatization, and preprocessing techniques such as
Bag of Words (BOW) model, enhancing skills in text data processing for NLP tasks.
2. Advanced NLP Techniques: Participants explored advanced NLP techniques like TF-
IDF as features, n-gram and channel models, practical NLTK library functions, and an
overview of recommender systems, improving proficiency in NLP feature engineering
and model building.
3. Advanced NLP and ML Concepts: This session aimed to provide a comprehensive
understanding of advanced NLP concepts like word embeddings, POS tagging, NER,
RNN, LSTM, LLM Models, text analysis techniques, Time Series Analysis, and basics
of MLOPs, preparing participants for complex NLP and ML tasks.

Detailed Report:

Week 8 focused on advanced topics in Natural Language Processing (NLP) and Machine
Learning (ML), essential for the Chatbot: Natural Language Processing Question Answering
for Building Conversational Agents project.

Day 1 delved into NLP basics, covering tokenization, stop words, normalization, stemming,
lemmatization, and preprocessing techniques such as Bag of Words (BOW) model, improving
text data processing skills for NLP tasks. Day 2 involved an assessment to evaluate
comprehension of these foundational NLP concepts.

Day 3 explored advanced NLP techniques like TF-IDF as features, n-gram and channel models,
practical NLTK library functions, and an overview of recommender systems, enhancing
proficiency in NLP feature engineering and model building. Day 4 included an assessment to
gauge understanding of these advanced NLP concepts.

On Day 5, participants gained a comprehensive understanding of advanced NLP concepts

including word embeddings, POS tagging, NER, RNN, LSTM, LLM Models, text analysis
techniques, Time Series Analysis, and basics of MLOPs, preparing them for complex NLP and

34 | P a g e
ML tasks. Day 6 concluded with tests assessing understanding of advanced NLP and ML
concepts.

Overall, Week 8 provided participants with a solid foundation in advanced NLP and ML
techniques, essential for developing sophisticated AI models like the Chatbot project.

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 Activity Log for the Ninth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

In-depth exploration of web

Session-22Web scraping & scraping techniques and
Machine Learning - machine learning projects,
Day-1
Classification & Regression covering both classification
projects explanation and regression tasks with
practical project
explanations.

An assessment which
Day-2 Daily Test-22 covers the previous session
topics

Thorough examination of
Deep Learning concepts
including Artificial Neural
Session-23-Deep Learning- Networks (ANN) and
Day-3
ANN & CNN ,Object Convolutional Neural
Detection projects explanation Networks (CNN),
accompanied by
explanations of object
detection projects.

An assessment which
Day-4 Daily Test-23 covers the previous session
topics

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 Detailed explanation of
Natural Language
Processing (NLP) projects
Day-5 Session 24-NLP & Time and Time Series analysis
Series projects explanation projects, covering
methodologies,
implementations, and
outcomes.
An assessment which
covers the previous session
Day-6 Daily Test-24, Python topics and Foundational
Prerequisite course understanding and
proficiency in the concepts

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 Weekly Report

Week – 9 (From: To: )

Objective of the Activity Done:

1. Web Scraping & ML Projects: The objective was to delve into web scraping
techniques and explain machine learning projects, focusing on classification and
regression tasks to enhance practical skills and project understanding.
2. Deep Learning Concepts & Object Detection: Participants aimed to gain a thorough
understanding of Deep Learning concepts such as Artificial Neural Networks (ANN),
Convolutional Neural Networks (CNN), and object detection projects, improving
knowledge and proficiency in advanced AI techniques.
3. NLP & Time Series Projects: The focus was on detailed explanations of Natural
Language Processing (NLP) projects and Time Series analysis projects, covering
methodologies, implementations, and outcomes to deepen understanding and
application of these techniques.

Detailed Report:

Week 9 comprised intensive sessions on advanced AI topics, including web scraping, machine
learning projects, Deep Learning concepts, NLP projects, and Time Series analysis.

Day 1 began with a session on web scraping techniques and machine learning projects,
providing insights into classification and regression tasks with practical project explanations.
Day 2 involved an assessment to evaluate comprehension of these topics.

On Day 3, participants delved into Deep Learning concepts like ANN, CNN, and object
detection projects, gaining a comprehensive understanding of advanced AI techniques. Day 4
included an assessment to gauge understanding of Deep Learning concepts and object
detection.

Day 5 focused on NLP projects and Time Series analysis, offering detailed explanations of
methodologies, implementations, and outcomes. This session aimed to enhance skills in NLP
techniques and Time Series analysis for real-world applications. Day 6 concluded with
assessments covering the week's topics and foundational Python concepts.

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 Activity Log for the Tenth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Comprehensive explanation
of project details,
methodologies,
Day-1 Session 25-Project
implementations, and
Explanation,Daily Test-25
outcomes,and the
assignment to practice this
session

Comprehensive explanation
of project details,
methodologies,
Day-2 Session 26-Project
implementations, and
Explanation,Daily Test-26
outcomes,and the
assignment to practice this
session

Comprehensive explanation
Day-3 Session-27-Project of project details,
explanation methodologies,
implementations.

Comprehensive explanation
Day-4 Session 28-Project of project details,
explanation methodologies,
implementations.

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Day-5 Assignment-1 Practical Knowledge

Revision for all the

Day-6 Grand Test-1 concepts by Practical
Knowledge

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 Weekly Report

Week – 10 (From: To: )

Objective of the Activity Done:

1. Sessions 25-28: The objective was to provide a comprehensive understanding of the

project, including details, methodologies, implementations, and outcomes, preparing
interns for practical application and assessment.
2. Assignment-1: The objective was to apply practical knowledge gained from project
explanations and sessions, reinforcing learning through hands-on tasks.
3. Grand Test-1: The objective was to revise and assess understanding of all concepts
covered throughout the internship, emphasizing practical knowledge and application.

Detailed Report:

Week 10 focused on a comprehensive review and understanding of the Chatbot project,

preparing interns for practical application and assessment.

Sessions 25-28 provided detailed explanations of the project, including methodologies,

implementations, and outcomes. Interns were given assignments to practice and reinforce their
understanding, culminating in Assignment-1 on Day 5.

Day 6 featured the Grand Test-1, aimed at revising and assessing interns' understanding of all
concepts covered throughout the internship. This test emphasized practical knowledge and
application, ensuring interns were well-prepared for the final stages of their internship.

Overall, Week 10 was crucial for consolidating knowledge and skills acquired during the
internship, preparing interns for practical application and assessment in the context of the
Chatbot: Natural Language Processing Question Answering for Building Conversational
Agents project.

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 Activity Log for the Eleventh Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Foundational
understanding and pro
Assignment-2,Python
Day-1 ciency in the
prerequisite course
concepts,Practical
Knowledge

Day-2 MET-1 Practical Knowledge

Assignment-3,March
Day-3 Practical Knowledge
Employibility test

Day-4 MET-2 Practical Knowledge

Day-5 Assignment-4 Practical Knowledge

Foundational
understanding and pro
MET-3,Machine Learning
Day-6 ciency in the
Prerequisite Course
concepts,Practical
Knowledge

42 | P a g e
 Weekly Report
Week – 11 (From: To: )

Objective of the Activity Done:

1. Assignment-2, Python Prerequisite Course: The objective was to enhance

foundational understanding and proficiency in Python programming, focusing on
practical knowledge and application.
2. MET-1: The objective was to gain practical knowledge and hands-on experience in a
specific task or project related to the internship.
3. Assignment-3, March Employability Test: The objective was to assess practical
knowledge and skills related to employability in the field of AI-ML.
4. MET-2: Similar to MET-1, the objective was to gain practical knowledge and hands-
on experience in a specific task or project related to the internship.
5. Assignment-4: The objective was to further enhance practical knowledge and skills
through a specific assignment related to the internship.
6. MET-3, Machine Learning Prerequisite Course: The objective was to solidify
foundational understanding and proficiency in machine learning concepts, focusing on
practical knowledge

Detailed Report:

Week 11 marked a continuation of practical learning and assessments to enhance foundational

knowledge and proficiency in Python programming, machine learning concepts, and practical
application related to the Chatbot: Natural Language Processing Question Answering for
Building Conversational Agents project.

Day 1 involved completing Assignment-2 in the Python prerequisite course, focusing on

strengthening foundational understanding and practical skills in Python programming. Day 2
included MET-1, providing hands-on experience and practical knowledge in a specific task or
project.

Day 3 encompassed completing Assignment-3 and taking the March Employability test,
assessing practical knowledge and skills relevant to employability in the AI-ML field. Day 4
continued with MET-2, offering further practical experience and knowledge enhancement.

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On Day 5, participants worked on Assignment-4, aimed at further improving practical
knowledge and skills in the internship-related tasks. Day 6 concluded the week with MET-3 in
the Machine Learning Prerequisite Course, reinforcing foundational understanding and
proficiency in machine learning concepts through practical applications.

Overall, Week 11 focused on practical learning and assessments to strengthen foundational

knowledge, practical skills, and employability readiness in the AI-ML domain, crucial for
success in the Chatbot project and future endeavors in the field.

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 Activity Log for the Twelveth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Foundational
understanding and pro
Assignment-5,Python
Day-1 ciency in the
Prerequisite course
concepts,Practical
Knowledge

Day-2 MET-4 Practical Knowledge

Day-3 Assignment-6 Practical Knowledge

Day-4 MET-5 Practical Knowledge

Day-5 Assignment-7 Practical Knowledge

Foundational
understanding and pro
MET-6, Machine Learning
Day-6 ciency in the
Prerequisite Course
concepts,Practical
Knowledge

45 | P a g e
 Weekly Report
Week – 12 (From: To: )

Objective of the Activity Done:

Assignments and METs: The objective was to enhance practical knowledge and proficiency
in Python programming and machine learning concepts through assignments and MET
sessions.

Detailed Report:

Week 12 marked the culmination of the internship program with a focus on enhancing practical
knowledge and proficiency in Python programming and machine learning concepts for the
Chatbot: Natural Language Processing Question Answering for Building Conversational
Agents project.

Day 1 involved completing Assignment-5, which aimed to strengthen foundational

understanding and proficiency in Python concepts. This assignment provided hands-on practice
and practical knowledge in Python programming.

Day 2 focused on MET-4, providing practical knowledge through hands-on activities and real-
world scenarios, reinforcing the concepts learned throughout the internship.

Day 3 continued with Assignment-6, further enhancing practical knowledge and problem-
solving skills in Python programming and machine learning concepts.

On Day 4, MET-5 further deepened practical knowledge through interactive sessions and
activities, consolidating learning outcomes and application skills.

Day 5 concluded with Assignment-7, emphasizing practical knowledge and application of

Python programming and machine learning concepts in real-world scenarios.

Day 6 wrapped up the week and the internship program with MET-6 and the Machine Learning
Prerequisite Course, enhancing foundational understanding and proficiency in machine
learning concepts and practical knowledge gained throughout the internship.

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 Activity Log for the Thirteenth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Foundational
understanding and pro
Assignment-8,Python
Day-1 ciency in the
Prerequisite course
concepts,Practical
Knowledge

Day-2 MET-7 Practical Knowledge

Day-3 Assignment-9 Practical Knowledge

Day-4 MET-8 Practical Knowledge

Day-5 Assignment-10 Practical Knowledge

Foundational
understanding and pro
Day-6 MET-9 ciency in the
concepts,Practical
Knowledge

47 | P a g e
 Weekly Report
Week – 13 (From: To: )

Objective of the Activity Done:

Assignments and MET Sessions: The objective was to enhance foundational understanding,
proficiency in Python concepts, and gain practical knowledge through assignments and MET
sessions.

Detailed Report:

Week 13 focused on consolidating and applying knowledge gained throughout the internship,
particularly in Python programming and practical application of concepts.

Day 1 involved Assignment-8 and continued with the Python Prerequisite course, aiming to
enhance foundational understanding and proficiency in Python concepts. This included tasks
to reinforce programming skills and practical knowledge.

Day 2 and Day 4 were dedicated to MET sessions (MET-7 and MET-8), providing opportunities
to apply theoretical knowledge to practical scenarios, further enhancing practical knowledge
and problem-solving skills.

Assignments continued on Day 3 and Day 5 (Assignment-9 and Assignment-10), reinforcing

learning through hands-on tasks and real-world applications and Day 6 concluded with MET-
9, focusing on reinforcing foundational understanding and proficiency in Python concepts
while emphasizing practical knowledge and application.

Overall, Week 13 was instrumental in solidifying Python programming skills, gaining practical
knowledge, and applying learned concepts to real-world scenarios, contributing significantly
to the development of skills required for the Chatbot: Natural Language Processing Question
Answering for Building Conversational Agents project.

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 Activity Log for the Fourteenth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Foundational
Day-1 Python Prerequisite course understanding and pro
ciency in theconcepts

Day-2 MET-10 Practical Knowledge

Python Prerequisite Foundational

Day-3 course,Machine learning understanding and pro
Prerequisite course ciency in theconcepts

Day-4 MET-11 Practical Knowledge

Foundational
understanding and pro
Day-5 MET-12 ciency in the
concepts,Practical
Knowledge

Revision for all the

Day-6 Final Grand Test concepts by Practical
Knowledge

49 | P a g e
 Weekly Report

Week – 14 (From: To: )

Objective of the Activity Done:

1. Python Prerequisite Course: The objective was to establish a foundational

understanding and proficiency in Python programming concepts essential for AI and
machine learning development.
2. Machine Learning Prerequisite Course: Participants aimed to gain foundational
knowledge and proficiency in machine learning concepts crucial for developing AI
models like the Chatbot project.
3. MET Sessions (10-12): These sessions focused on applying practical knowledge
gained from the Python and machine learning courses to real-world scenarios,
enhancing hands-on skills and problem-solving abilities.
4. Final Grand Test: The final objective was to revise and consolidate all learned
concepts through a comprehensive test, ensuring a thorough understanding and
readiness for applying these skills in practical projects.

Detailed Report:

Week 14 marked the culmination of the internship with a focus on revising and consolidating
all learned concepts through practical application and assessments.

Day 1 and Day 3 involved revisiting Python and machine learning prerequisite courses,
emphasizing foundational understanding and proficiency in these key areas. Participants
engaged in exercises and practical tasks to reinforce learning.

MET sessions on Day 2, Day 4, and Day 5 provided opportunities for applying practical
knowledge gained from the courses to real-world scenarios. These sessions aimed to enhance
problem-solving skills and hands-on experience in AI and machine learning tasks.

The week concluded with the Final Grand Test on Day 6, where participants revised all
concepts learned throughout the internship and applied them in a comprehensive assessment.
This test ensured that interns were well-prepared and equipped with the necessary skills and
knowledge to tackle complex projects like the Chatbot: Natural Language Processing Question
Answering for Building Conversational Agents.

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 Activity Log for the Fifteenth Week

Person In-
Brief description of the daily
Day / Hrs Learning Outcome Charge
activity
Siignature

Comprehensive explanation
Day-1 Project Explanation
of project details

Comprehensive explanation
Day-2 Project Explanation
of project details

Comprehensive explanation
Day-3 Project Explanation
of project details

Day-4

Day-5

Day-6

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 Weekly Report

Week – 15 (From: To: )

Objective of the Activity Done:

The objective of Week 15's activities was to provide a thorough and detailed explanation of
the project, focusing on the Chatbot: Natural Language Processing Question Answering for
Building Conversational Agents project.

Detailed Report:

During Week 15, the focus was on providing a comprehensive understanding of the Chatbot
project, including its objectives, methodologies, technologies used, and the overall process of
Natural Language Processing (NLP) for building conversational agents.

Day 1 involved a detailed explanation of the project's background, objectives, and the
importance of NLP in developing conversational agents. Key components such as data
preprocessing, language modeling, intent recognition, and response generation were
discussed.

Day 2 continued the project explanation, diving deeper into the technical aspects such as the
implementation of NLP algorithms, neural network architectures for text processing, and the
integration of AI frameworks for chatbot development.

On Day 3, the project explanation was further elaborated, covering topics like user interaction
design, backend system integration, testing methodologies, and future enhancements planned
for the chatbot system.

Overall, Week 15's activities aimed to ensure a comprehensive understanding of the Chatbot
project's scope, technologies, and methodologies, laying the groundwork for successful
implementation and evaluation.

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 Chapter 5: Outcomes Description

During my internship at the International Institute of Digital Technologies (IIDT) working on

the project "Chatbot: Natural Language Processing Question Answering for Building
Conversational Agents," I experienced a highly conducive work environment that facilitated
learning, collaboration, and professional growth.

The work environment at IIDT was characterized by clear job roles and responsibilities,
established protocols, and well-defined processes. This clarity helped in understanding
expectations and navigating tasks efficiently. Additionally, there was a strong emphasis on
discipline and time management, evident from the structured schedule of classes every alternate
day, regular assignments, daily tests, and the monthly employability test. This structure not
only ensured consistent learning but also instilled a sense of accountability and responsibility.

Interactions within the team and with supervisors were marked by mutual support, teamwork,
and open communication. Collaborative projects provided opportunities to work closely with
peers, share knowledge, and leverage collective expertise. This collaborative spirit fostered
harmonious relationships and a positive work culture, enhancing productivity and creativity.

The facilities provided by IIDT were well-maintained, creating a comfortable and conducive
workspace. The availability of resources, including access to cutting-edge technologies and
software tools, enabled hands-on learning and practical application of AI-ML concepts. The
physical space and ventilation were adequate, contributing to a pleasant working environment
conducive to focused and productive work sessions.

Motivation was also a key aspect of the work environment. The challenging yet supportive
atmosphere motivated interns to push their boundaries, explore innovative solutions, and strive
for excellence in their projects. Regular feedback sessions and mentorship opportunities further
fueled motivation and personal development.

Overall, the internship experience at IIDT provided a holistic and enriching learning
environment, characterized by clear communication, mutual support, effective time
management, and a focus on professional growth. These aspects collectively contributed to a
fulfilling and impactful internship experience in the field of AI-ML and conversational AI
development.

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Describe the real time technical skills you have acquired

Throughout the internship at the International Institute of Digital Technologies (IIDT) focusing
on "Chatbot: Natural Language Processing Question Answering for Building Conversational
Agents," I acquired a range of real-time technical skills that are highly relevant to job roles in
the AI-ML domain. These skills were developed through hands-on experience, practical
assignments, and regular assessments, contributing significantly to my professional growth and
readiness for AI-ML roles.

One of the key technical skills I acquired during the internship is proficiency in Natural
Language Processing (NLP) techniques. Through practical exercises and coding sessions, I
gained a deep understanding of NLP algorithms such as sentiment analysis, named entity
recognition, and text summarization. This hands-on experience enabled me to implement these
algorithms effectively in developing conversational agents capable of understanding and
responding to natural language queries.

Additionally, I enhanced my knowledge and practical skills in machine learning frameworks

such as TensorFlow and PyTorch. These frameworks were extensively used in the development
and training of AI models for the chatbot project. I gained experience in building neural
networks, fine-tuning models, and optimizing performance metrics, thereby strengthening my
expertise in AI-ML methodologies and practices.

Furthermore, the internship provided valuable exposure to real-world AI challenges and project
scenarios. Collaborating with team members on project assignments allowed me to apply
theoretical knowledge to solve practical problems, improving my problem-solving abilities and
decision-making skills in the context of AI-ML projects.

The regular assessments, including daily tests and the monthly employability test, played a
crucial role in evaluating and consolidating my technical skills. These assessments not only
measured my understanding of AI-ML concepts but also encouraged continuous learning and
improvement throughout the internship period.

Overall, the outcomes of this internship include a comprehensive skill set in NLP, machine
learning frameworks, problem-solving, and project collaboration, all of which are essential for
success in AI-ML roles within the industry. This hands-on experience and technical expertise

54 | P a g e
gained during the internship have significantly contributed to my professional development
and readiness for future endeavors in the field of AI-ML.

Describe the managerial skills you have acquired

During my internship at the International Institute of Digital Technologies (IIDT), working on

the project "Chatbot: Natural Language Processing Question Answering for Building
Conversational Agents," I acquired a range of managerial skills that significantly contributed
to my professional development. These skills spanned various areas such as planning,
leadership, teamwork, behavior, workmanship, productive use of time, weekly improvement
in competencies, goal setting, decision making, and performance analysis, all within the
context of AI-ML.

One of the key managerial skills I honed during this internship was effective planning. Through
regular classes held every alternate day, I learned to organize my time efficiently to balance
coursework, project assignments, and personal development activities. This involved creating
detailed schedules and timelines to ensure timely completion of tasks and meeting project
milestones.

Leadership skills were another area of growth during the internship. Working collaboratively
with team members on project assignments allowed me to take on leadership roles, delegate
tasks effectively, and ensure the team's progress towards common goals. I also learned to
motivate and inspire team members, fostering a positive and productive work environment
conducive to creativity and innovation.

Teamwork played a crucial role in the success of our projects. Through group discussions,
brainstorming sessions, and collaborative problem-solving, I developed strong interpersonal
skills and the ability to work harmoniously with diverse teams. This experience enhanced my
communication skills, empathy, and adaptability, essential qualities for effective teamwork in
AI-ML projects.

In terms of behavior and workmanship, I consistently demonstrated a high level of

professionalism, dedication, and attention to detail in all tasks undertaken during the internship.
I maintained a positive attitude, sought feedback for continuous improvement, and adhered to

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best practices in AI-ML development, contributing to the overall quality of our project
outcomes.

Productive use of time was a fundamental aspect of my internship experience. I optimized my

time management skills to prioritize tasks, minimize distractions, and focus on activities that
aligned with my learning objectives and project goals. This approach enabled me to achieve
significant progress and milestones throughout the internship period.

Weekly improvement in competencies was facilitated by the structured learning environment

at IIDT. Continuous assessment through daily tests and assignments helped me identify areas
for improvement and implement targeted strategies to enhance my skills in AI-ML concepts,
programming languages, and NLP techniques.

Goal setting and decision making were integral to project planning and execution. I set clear,
achievable goals for each phase of the project, evaluated progress regularly, and made informed
decisions to overcome challenges and optimize outcomes. This experience strengthened my
strategic thinking, problem-solving abilities, and decision-making skills in AI-ML project
management.

Performance analysis was a critical aspect of the internship, culminating in the monthly
employability test. This test provided valuable feedback on my technical skills, soft skills, and
overall readiness for AI-ML roles in the industry. I used this feedback to assess my strengths,
identify areas for development, and refine my career objectives for future growth and success.

My internship experience at IIDT provided a comprehensive platform for developing and

refining essential managerial skills in the context of AI-ML projects. From effective planning
and leadership to teamwork, behavior, workmanship, productive time use, competency
improvement, goal setting, decision making, and performance analysis, I gained invaluable
insights and capabilities that will positively impact my professional journey in the field of
Artificial Intelligence and Machine Learning.

Describe how you could improve your communication skills

Throughout the internship project focused on "Chatbot: Natural Language Processing Question
Answering for Building Conversational Agents," significant improvements were observed in
various aspects of communication skills. The internship, facilitated by the International

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Institute of Digital Technologies (IIDT), provided a structured environment conducive to
honing these skills through classes, assignments, daily tests, and a monthly employability test.

One of the notable improvements was in oral communication. Regular participation in virtual
classes and team discussions enhanced the ability to express ideas clearly and confidently. The
interactive nature of the sessions allowed for practicing extempore speech and articulating key
points effectively, leading to a noticeable increase in the ability to communicate complex AI-
ML concepts fluently.

Similarly, written communication skills were refined through project documentation, report
writing, and assignment submissions. The emphasis on clear and concise communication in
technical documents improved the ability to convey information accurately, which is crucial in
AI-ML projects where precision is paramount.

Conversational abilities were also enhanced during the internship. Engaging in discussions
with peers and mentors on AI-ML topics not only improved understanding but also contributed
to developing a conversational style that is both informative and engaging. The experience of
collaborating with diverse teams further enriched the ability to understand others' perspectives
and communicate effectively in different contexts.

Furthermore, the internship provided opportunities to work on anxiety management while

communicating. Presenting project updates, participating in group discussions, and engaging
in Q&A sessions helped in building confidence levels and managing anxiety associated with
public speaking and group interactions. Learning to maintain niceties and protocols during
professional communications, such as greeting, thanking, and appreciating others, was also
emphasized and practiced throughout the internship.

The internship at IIDT significantly contributed to improving communication skills in various

aspects, including oral and written communication, conversational abilities, confidence levels,
anxiety management, understanding others, getting understood by others, extempore speech,
ability to articulate key points, closing conversations, and maintaining professional protocols.
These enhanced communication skills are valuable assets that will continue to benefit in future
endeavors within the AI-ML domain and professional settings.

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Describe how could you could enhance your abilities in group discussions, participation
in teams, contribution as a team member, leading a team/activity

During the internship at the International Institute of Digital Technologies (IIDT) focusing on
AI-ML and the project "Chatbot: Natural Language Processing Question Answering for
Building Conversational Agents," several outcomes were achieved that significantly enhanced
my abilities in various aspects of teamwork and leadership.

One of the key outcomes was the improvement in my ability to participate effectively in group
discussions. Through collaborative projects and interactive sessions during classes, I learned
how to engage constructively in discussions, share ideas, and actively listen to others'
perspectives. This experience helped me develop better communication skills and the
confidence to express my thoughts in a team setting.

As a team member, I contributed actively to project assignments by leveraging my knowledge

of AI-ML concepts and NLP techniques. Collaborating with teammates allowed me to
understand the importance of teamwork in achieving common goals. I learned to appreciate
diverse viewpoints and adapt my approach to work harmoniously within a team, ensuring that
each member's strengths were utilized effectively.

Moreover, the internship provided opportunities to lead team activities and take on leadership
roles in project tasks. I gained valuable experience in organizing team efforts, delegating
responsibilities, and coordinating workflow to achieve project milestones. This experience not
only enhanced my leadership skills but also taught me the importance of effective delegation,
time management, and task prioritization in a team environment.

Participating in the monthly employability test further strengthened my ability to perform under
pressure and showcase my skills and knowledge effectively. It was a valuable experience that
helped me understand the expectations of industry-standard assessments and prepared me for
future job interviews and professional engagements.

The internship at IIDT played a crucial role in enhancing my abilities in group discussions,
participation in teams, contribution as a team member, and leading team activities. It provided
a platform to apply theoretical knowledge in practical scenarios, fostering a holistic
understanding of AI-ML concepts while honing essential soft skills necessary for success in
the field.

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Describe the technological developments you have observed and relevant to the subject
area of training

During my internship at the International Institute of Digital Technologies (IIDT), focusing on

AI-ML and specifically on the project "Chatbot: Natural Language Processing Question
Answering for Building Conversational Agents," I encountered several impactful outcomes
related to technological advancements in digital technologies pertinent to my job role.

One of the key outcomes was a deep understanding and practical application of Natural
Language Processing (NLP) techniques within the context of building conversational agents.
This included gaining proficiency in algorithms such as sentiment analysis, named entity
recognition, and intent classification, which are fundamental in enabling chatbots to understand
and respond to user queries effectively.

Furthermore, significant developments in the integration of AI and Machine Learning (ML)

models with chatbot frameworks. This involved leveraging pre-trained language models like
BERT and GPT-3 to enhance the conversational capabilities of chatbots, enabling them to
generate more contextually relevant and human-like responses.

Additionally, I gained insights into the advancements in speech recognition technology, which
plays a crucial role in enabling voice-based interactions with chatbots. This included exploring
tools and libraries such as SpeechRecognition and Google Cloud Speech-to-Text,
understanding their capabilities, and integrating them into chatbot systems for enhanced user
experiences.

Moreover, advancements in the deployment of chatbots across multiple platforms, including

web applications, messaging platforms, and voice assistants. This encompassed learning about
deployment strategies using cloud services like AWS Lambda and Azure Functions, as well as
optimizing chatbot performance and scalability for diverse user interactions.

Overall, these outcomes signify the continuous evolution and innovation in digital
technologies, particularly in the realm of AI-ML and chatbot development. The internship
provided a valuable opportunity to witness and contribute to these technological advancements,
enhancing my skills and knowledge in creating intelligent conversational agents.

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 Student Self Evaluation of the Short-Term Internship

Student Name: Registration No:

Term of Internship: From: To:
Date of Evaluation:
Organization Name & Address:

Please rate your performance in the following areas:

Rating Scale: Letter grade of CGPA calculation to be provided

1 Oral Communication 1 2 3 4 5
2 Written Communication 1 2 3 4 5
3 Proactiveness 1 2 3 4 5
4 Interaction Ability with Community 1 2 3 4 5
5 Positive Attitude 1 2 3 4 5
6 Self – confidence 1 2 3 4 5
7 Ability to learn 1 2 3 4 5
8 Work Plan and Organization 1 2 3 4 5
9 Professionalism 1 2 3 4 5
10 Creativity 1 2 3 4 5
11 Quality of work done 1 2 3 4 5
12 Time Management 1 2 3 4 5
13 Understanding the Community 1 2 3 4 5
14 Achievement of Desired Outcomes 1 2 3 4 5
15 OVERALL PERFORMANCE 1 2 3 4 5

Date: Signature of the Student

Evaluation by the Supervisor of the Intern Organization

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 PHOTOS & VIDEO LINKS

Fig .1 Code used to develop project along with its output

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EVALUATION

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 Internal Evaluation for Short Term Internship
(On-site/Virtual)
Objectives:

• To integrate theory and practice.

• To learn to appreciate work and its function towards the future.
• To develop work habits and attitudes necessary for job success.
• To develop communication, interpersonal and other critical skills in the
future job.
• To acquire additional skills required for the world of work.
Assessment Model:

• There shall only be internal evaluation.

• The Faculty Guide assigned is in-charge of the learning activities of the
students and for the comprehensive and continuous assessment of the
students.
• The assessment is to be conducted for 100 marks.
• The number of credits assigned is 4. Later the marks shall be converted
into grades and grade points to include finally in the SGPA and CGPA.
• The weightings shall be:
o Activity Log 25 marks o Internship Evaluation
50marks
o Oral Presentation 25 marks
• Activity Log is the record of the day-to-day activities. The Activity Log
is assessed on an individual basis, thus allowing for individual members
within groups to be assessed this way. The assessment will take into
consideration the individual student’s involvement in the assigned
work.
• While evaluating the student’s Activity Log, the following shall be
considered –

a. The individual student’s effort and commitment.

b. The originality and quality of the work produced by the individual

student.

c. The student’s integration and co-operation with the work assigned.

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 d. The completeness of the Activity Log.

• The Internship Evaluation shall include the following components and

based on Weekly Reports and Outcomes Description a. Description of
the Work Environment.

b. Real Time Technical Skills acquired.

c. Managerial Skills acquired.

d. Improvement of Communication Skills.

e. Team Dynamics

f. Technological Developments recorded.

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 INTERNAL ASSESSMENT STATEMENT

Name Of the Student:

Programme of Study:

Year of Study:

Group:

Register No/H.T. No:

Name of the College:

University:

S. No Evaluation Criterion Maximum Marks

Marks Awarded
1. Activity Log 25
2. Internship Evaluation 50
3. Oral Presentation 25
GRAND TOTAL 100

Date: Signature of the Faculty Guide

Certified by

Date: Signature of head of department/principal

Seal:

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 EXTERNAL ASSESSMENT STATEMENT

Name Of the Student:

Programme of Study:

Year of Study:

Group:

Register No/H.T. No:

Name of the College:

University:

S. No Evaluation Criterion Maximum Marks

Marks Awarded
1. Internship Evaluation 25
2. For the grading giving by the Supervisor of the 50
Intern Organization
3. Viva-Voce 25
4. Total 150
GRAND TOTAL (EXT. 50 M + INT. 100M) 200

Signature of the Faculty Guide

Signature of the Internal Expert

Signature of the External Expert

Signature of the Principal with Seal

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