Summer Training 2026

Applied Gen AI Developer

Applied Gen AI Developer

Training Duration 29th June 2026 – 31st July 2026
Training Topic Applied Gen AI Developer
Subject Code ES 361
Class B.Tech. II Year

Faculty Instructors

  • Dr. Alka Leekha
  • Dr. Kavita Bhatt

Course Overview

To equip participants with the theoretical knowledge and practical skills to design, develop, test, and deploy real-world Generative AI applications using LLMs, prompt engineering, RAG, embeddings, vector databases, AI agents, LangGraph, MCP, and multi-agent systems, while promoting responsible and ethical AI practices.

Objectives of the Training

  • Educate on Key Concepts: Participants will learn and understand about the fundamental concepts of Generative AI, LLMs, RAG, MCP, and Multi-Agent Systems.
  • Practical Skills: Training aims to equip participants with practical skills to apply prompt engineering techniques to improve the performance and reliability of LLM-based applications.
  • Hands-On Experience: Interactive sessions and live demonstrations allow participants to develop AI agents and workflows using tools such as LangGraph and MCP, and implement embeddings, vector databases, and RAG pipelines for context-aware AI solutions.
  • Real-World Applications: Students will develop real-world Generative AI solutions such as clinical decision support, legal research, AI-powered marketplaces, educational assistants, customer support, financial analysis, and business automation systems.

Skills Acquired

  • Generative AI & LLMs: Understanding and application of LLM-based solutions.
  • Prompt Engineering: Designing effective prompts for reliable AI outputs.
  • RAG & Vector Databases: Building context-aware knowledge-based AI applications.
  • AI Agents & Multi-Agent Systems: Developing intelligent agents for task automation and decision-making.
  • LangGraph & MCP: Creating AI workflows and integrating external tools.
  • AI Application Development: Designing and developing real-world Generative AI solutions.

Conclusion

The Applied Generative AI Developer Training provided participants with a strong combination of theoretical knowledge and hands-on experience in modern Generative AI technologies. Through practical exercises and real-world projects, participants developed the ability to design, implement, test, and deploy AI-driven solutions using LLMs, RAG, AI agents, LangGraph, MCP, and multi-agent systems. The training also strengthened problem-solving, collaboration, and Responsible AI skills, preparing participants to apply Generative AI effectively in real-world scenarios through practical project work.

AI ML and DL using Python

AI, ML and DL using Python

Training Duration 29th June 2026 – 31st July 2026
Training Topic AI, ML, and DL using Python
Subject Code ES 361
Class B.Tech. III Year

Faculty Instructors

  • Dr. Mahesh Kumar
  • Ms. Nisha Malhotra
  • Ms. Neetu Singh
  • Ms. Kajal Kaul

Course Overview

This five-week Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) Training Program was designed to equip students with practical skills and project-based learning in AI, ML, and DL. The updated program emphasized Python programming, machine learning, deep learning and natural language processing. Students worked with real-world datasets and developed AI-based solutions through hands-on activities, demonstrations, and projects.

Objectives of the Training

The primary objective of the five-week Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) Training Program was to provide students with a strong foundation in programming, data analytics, machine learning, and deep learning through a combination of logical concepts and hands-on implementation. The specific objectives were:

  • To develop fundamental programming skills in Python, including variables, data types, operators, functions, conditions, loops, lists, tuples, sets, and dictionaries.
  • To provide practical knowledge of NumPy, Pandas, data preprocessing, statistical measures, and data visualization.
  • To introduce students to the complete machine learning workflow, including exploratory data analysis (EDA), feature engineering, model building, and model evaluation.
  • To provide hands-on implementation of supervised and unsupervised machine learning algorithms, including Linear Regression, Logistic Regression, KNN, Decision Trees, Random Forest, K-Means, SVM, and Naïve Bayes.
  • To develop an understanding of Deep Learning architectures, including ANN, DNN, CNN, RNN, LSTM, and GRU.
  • To introduce advanced deep learning concepts such as Transfer Learning and Pre-trained Models, including VGG16, ResNet, and MobileNet.
  • To provide practical exposure to Natural Language Processing (NLP) and its implementation using suitable AI/ML techniques.
  • To encourage students to apply their knowledge through projects, graded quizzes, evaluations, and project presentations.
  • To enhance students’ problem-solving, analytical, programming, and research-oriented skills for real-world AI applications.

Skills Acquired

At the completion of the training, students acquired the following technical and practical skills:

Python and Data Analytics

  • Python programming fundamentals and problem-solving.
  • Working with NumPy and Pandas for numerical computation and data manipulation.
  • Data preprocessing, exploratory data analysis (EDA), and feature engineering.
  • Calculation and interpretation of statistical measures.
  • Data visualization and graphical representation of datasets.

Machine Learning

  • Understanding of the machine learning pipeline, from data preprocessing to model evaluation.
  • Implementation of Linear Regression and Logistic Regression.
  • Classification using K-Nearest Neighbors (KNN), Decision Trees, Random Forest, SVM, and Naïve Bayes.
  • Unsupervised learning using K-Means Clustering.
  • Understanding of feature engineering and selection.
  • Evaluation of ML models using appropriate performance metrics.

Deep Learning

  • Understanding of neural networks and their working principles.
  • Implementation and understanding of ANN and DNN architectures.
  • Practical exposure to CNN for image-based applications.
  • Understanding of sequential models including RNN, LSTM, and GRU.
  • Exposure to Transfer Learning and pre-trained models, including VGG16, ResNet, and MobileNet.

Natural Language Processing

  • Introduction to fundamental NLP concepts and techniques.
  • Practical implementation of NLP-based applications.
  • Understanding of how AI techniques can be applied to text and language-based problems.

Project and Professional Skills

  • Working with real-world datasets and developing AI-based solutions.
  • Applying appropriate ML/DL algorithms to solve practical problems.
  • Model evaluation, interpretation, and performance comparison.
  • Project development, documentation, presentation, and communication.
  • Improved analytical thinking, problem-solving, teamwork, and technical presentation skills.

3. Conclusion

The five-week AI, ML, and DL training program provided students with a comprehensive and practical learning experience covering the complete journey from Python programming and data preprocessing to Machine Learning, Deep Learning, Transfer Learning, and NLP. Through hands-on sessions, graded quizzes, projects, evaluations, and presentations, students gained practical exposure to implementing AI techniques on real-world problems. The training strengthened their programming, analytical, problem-solving, and model-development capabilities and prepared them to undertake advanced academic projects, research work, internships, hackathons, and future careers in AI, ML, and Deep Learning.

Data Analytics and Data Science

Data Analytics and Data Science

Subject Code ES 361
Class B.Tech. III Year
Duration 29th June 2026 – 31st July 2026

Faculty Coordinators

  • Dr. Kirti Sharma
  • Ms. Nupur Chugh

Course Overview

The Data Analytics and Data Science Training Programme is designed to develop students’ theoretical understanding and practical skills in data analysis, data science, and data-driven decision-making. The programme provides hands-on exposure to Python, NumPy, Pandas, SQL, Power BI, data visualization, and basic machine learning techniques, using real-world datasets and practical problem-solving.

Objectives

  • To develop fundamental knowledge of Data Analytics and Data Science.
  • To provide hands-on experience with Python, SQL, Power BI, and data science tools.
  • To develop skills in data preprocessing, exploratory data analysis, visualization, and interpretation.
  • To introduce students to basic machine learning and predictive analytics.
  • To enable students to solve real-world data-driven problems.

Skills Acquired

  • Python, NumPy and Pandas
  • SQL and Data Analysis
  • Data Cleaning and Exploratory Data Analysis
  • Data Visualization and Power BI Dashboards
  • Statistical Analysis
  • ML Algorithms: KNN, Random Forest, feature scaling, and cross-validation.
  • Deep Learning: Introduction to neural networks, CNNs, TensorFlow & Keras.
  • Data Interpretation and Insight Generation

Methodology

The training combines interactive lectures, hands-on exercises, real-world datasets, practical assignments, and mini-projects covering Data Analytics, Data Science, Python, SQL, Power BI, and basic Machine Learning.

Learning Outcomes

Students will be able to perform data preprocessing and analysis, manipulate datasets using Python and SQL, develop interactive Power BI dashboards, apply basic machine learning techniques, and derive meaningful insights from real-world data.

Conclusion

The programme provides industry-oriented exposure to Data Analytics and Data Science, preparing students for opportunities in Data Analytics, Data Science, Business Intelligence, and related domains.

Advanced Data Structures and Introduction to GenAI

Advanced Data Structures and Introduction to GenAI

(In Collaboration with Coding Ninjas Pvt. Ltd.)

Training Duration 29th June 2026 – 31st July 2026
Training Topic Advanced Data Structures and Introduction to GenAI
Subject Code ES 361
Class B.Tech. II Year

Faculty Instructors

  • Dr. Ajay Dureja
  • Ms. Akansha

Course Overview

The Advanced Data Structures and Introduction to Generative Artificial Intelligence (GenAI) course is designed to provide students with a strong foundation in advanced problem-solving techniques, efficient data organization, algorithmic thinking, and emerging Generative AI technologies. Conducted in collaboration with Coding Ninjas Pvt. Ltd., the training combines core computer science concepts with industry-oriented learning and practical exposure.

The course covers advanced data structures and their applications in developing efficient algorithms and solving complex computational problems. Alongside this, students are introduced to the fundamentals of Generative AI, including the concepts behind modern AI systems, Large Language Models (LLMs), prompt engineering, and practical applications of GenAI.

The training emphasizes hands-on learning through coding exercises, problem-solving activities, demonstrations, and practical applications, enabling students to connect theoretical concepts with real-world computing scenarios.

Objectives of the Training

The major objectives of the training are to:

  1. Develop a strong understanding of advanced data structures and their practical applications.
  2. Enhance students’ algorithmic thinking, logical reasoning, and computational problem-solving abilities.
  3. Familiarize students with efficient techniques for handling complex data and designing optimized algorithms.
  4. Provide practical exposure to coding and implementation of advanced data structures.
  5. Introduce students to the fundamentals and applications of Generative Artificial Intelligence.
  6. Bridge the gap between academic concepts and industry requirements through industry-oriented training in collaboration with Coding Ninjas Pvt. Ltd.

Skills Acquired

Upon successful completion of the training, students are expected to acquire the following skills:

  • Advanced Data Structure Skills: Understanding and implementation of trees, graphs, heaps, hash tables, advanced searching and sorting techniques, and other relevant data structures.
  • Algorithmic Problem Solving: Ability to analyze computational problems and develop efficient solutions.
  • Complexity Analysis: Understanding and application of time and space complexity for evaluating algorithms.
  • Programming Skills: Improved coding proficiency through hands-on implementation and practice.
  • Optimization Skills: Ability to select appropriate data structures and algorithms to improve program efficiency.
  • Generative AI Fundamentals: Understanding the basic concepts, capabilities, and applications of Generative AI.
  • Industry Readiness: Exposure to contemporary technologies and practices relevant to software development and AI-enabled applications.

Conclusion

The Advanced Data Structures and Introduction to GenAI training provides students with a balanced combination of strong computer science fundamentals and exposure to emerging artificial intelligence technologies. The collaboration with Coding Ninjas Pvt. Ltd. adds an industry-oriented dimension to the training, helping students understand how programming, algorithms, and Generative AI are applied in modern technology environments.

By the end of the course, students will be better equipped to solve complex programming problems, design efficient algorithms, use advanced data structures effectively, and explore Generative AI tools for software development and productivity. The training will therefore contribute significantly towards strengthening students’ technical competency, problem-solving ability, innovation mindset, and industry readiness.

Advanced Embedded, IoT and AI

Advanced Embedded, IoT and AI

Training Duration 29th June 2026 – 31st July 2026
Training Topic Advanced Embedded, IoT and AI
Subject Code ES 361
Class EEE, ECE III Year

Faculty Instructors

  • Mr. Arman Hussain
  • Mr. Chandrav Sourav

Course Overview

This training program helps students build practical skills in Advanced IoT using ESP, Raspberry Pi, and FreeRTOS. Students get hands-on experience with IoT hardware, embedded programming, sensor integration, device communication, and real-world IoT projects.

Objectives of Training

  • Understand IoT Fundamentals: Participants will learn the basic ideas of IoT, embedded systems, and connected devices.
  • ESP-Based Development: Gain hands-on experience with ESP boards, sensors, actuators, and wireless communication.
  • Raspberry Pi Applications: Learn to use Raspberry Pi for IoT projects, device control, data processing, and automation.
  • FreeRTOS & Embedded Systems: Build an understanding of FreeRTOS, task management, and real-time embedded programming.
  • Hands-On Projects: Apply the concepts through practical experiments and real-world IoT projects that involve hardware and software integration.

These objectives aim to give students a strong foundation in IoT, ESP, Raspberry Pi, and FreeRTOS. This will prepare them to develop and implement real-world smart and connected systems.

Skills Acquired

  • ESP32 Fundamentals: Basic programming, GPIO, Wi-Fi, and device interfacing.
  • Sensors: Understanding and connecting common sensors with microcontrollers.
  • FreeRTOS & Embedded Concepts: Basics of FreeRTOS, millis(), interrupts, and real-time programming.
  • Programming: Basics of C, C++, and Python for IoT and embedded applications.
  • Raspberry Pi & AI Integration: Basic integration of Raspberry Pi with AI applications.
  • Hands-On Projects: Practical experience from real-world IoT and embedded system projects.

Conclusion

The Advanced IoT training gave students an opportunity to learn and explore ESP32, sensors, FreeRTOS, Raspberry Pi, and programming with C, C++, and Python through practical activities. By working on hands-on projects, students were able to understand how different hardware and software components work together to create real-world IoT solutions. Overall, the training helped students build practical skills and confidence in IoT, embedded systems, and AI-integrated applications.

MERN

MERN Stack Development

(In Collaboration with Brain Mentors Pvt. Ltd.)

Training Duration 29th June 2026 – 31st July 2026
Training Topic MERN
Subject Code ES 361
Class B.Tech. II Year

Faculty Instructors

  • Dr. Sandeep Sharma

Course Overview

This summer training program was conducted to equip engineering students with hands-on skills in MERN Stack (MongoDB, Express.js, React.js, Node.js) and AI Integration, enabling them to build real-world full-stack applications enhanced by artificial intelligence.

Objectives of the Training

  • Build responsive web apps using MERN stack.
  • Understand frontend-backend architecture.
  • Create and consume REST APIs.
  • Integrate Python-based AI models with Node/React.
  • Host projects using cloud platforms (Vercel, Render, AWS).
  • Collaborate using Git & GitHub.

Skills Acquired

  • Full Stack Web Development
  • React & Node.js Integration
  • MongoDB Modeling
  • RESTful API Design
  • AI Integration with Python
  • Git & GitHub Workflow
  • Cloud Deployment and CI/CD Basics

Conclusion

This 5-week summer training organized by Brain Mentors provided a project-driven, real-world learning experience for B.Tech students of BVP. Students not only strengthened their MERN stack foundations but also gained hands-on exposure to integrating AI modules into full-stack applications—making them industry-ready for future placements and internships.

Training 2025

Summer Training 2025

AI, ML, and DL using Python

Training Duration: 23rd June 2025 – 25th July 2025

Training Topic: AI, ML, and DL using Python

Subject Code: ES 361

Class: B.Tech. III Year

Faculty Instructors

  • Dr. Arun Kumar Dubey
  • Dr. Achin Jain
  • Ms. Neha Gupta
  • Dr. Payal Malik
  • Ms. Neha Sharma

Course Overview

This training program is designed to help students master skills in Artificial Intelligence, Machine Learning, and Deep Learning. The students gained hands-on experience by working on real-world AI projects.

Objectives of the Training

  • Educate on Key Concepts: Participants will learn about the fundamental concepts of AI and ML, including data processing, model building, and ethical considerations.
  • Practical Skills: Training aims to equip participants with practical skills to apply AI/ML concepts in real-world scenarios.
  • Hands-On Experience: Interactive sessions and live demonstrations allow participants to gain hands-on experience with AI/ML tools and technologies.
  • Real-World Applications: Training covers various applications of AI and ML across different industries, providing insights into how these technologies can be utilized.

These objectives ensure that students are well-prepared to leverage AI and ML in their professional endeavors and to contribute to the ongoing advancements in these fields.

Skills Acquired

  • Python Fundamentals: Core concepts such as functions, strings, lists, tuples, sets, loops, and conditional statements.
  • Statistical and Visualization Libraries: Hands-on experience with NumPy, Pandas, Matplotlib, and exposure to visualization platforms.
  • Machine Learning Algorithms:
    • Supervised Learning: Linear regression, logistic regression, support vector machines, decision trees, and random forests.
    • Unsupervised Learning: K-means clustering.
  • Deep Learning Architectures: ANN, DNN, CNN, RNN, LSTM, GRU, with discussions on loss functions, optimizers, and key hyperparameters.

Conclusion

Students applied ML and DL techniques to real-world problems, showcasing their understanding and technical skills through practical project work.

Electric Vehicle and Battery Management Systems

Coordinators: Dr. Bharat Singh and Dr. Ashutosh Gupta

Training Duration: 23rd June 2025 – 25th July 2025

Training Topic: Electric Vehicle and Battery Management Systems

Subject Code: ES 361

Class: B.Tech. III Year

Course Overview

The program aimed to build a strong foundation in electric vehicle technology and encourage interdisciplinary learning in the field of sustainable transportation.

Objectives of the Training

To equip students with the fundamentals and advancements in electric mobility, the program focused on EV architecture, lithium-ion battery technology, and BMS design and operation.

Skills Acquired

The successful conversion of a mechanical bicycle into an electric cycle using a 250W, 36V BLDC hub motor, a Li-ion battery pack (14S3P configuration), and essential control components including a sine-wave motor controller, Pedal Assist Sensor (PAS), throttle, Battery Management System (BMS), and e-brake levers. This hands-on project enabled students to understand the integration and functioning of electric drivetrain components. In addition, students explored the control and wiring systems of an electric scooter (Bajaj Chetak 3503), gaining real-time experience in EV subsystem coordination. The program also covered key BMS functionalities such as SOC/SOH estimation, temperature and voltage monitoring, fault detection, thermal management, and cell balancing techniques, supported by circuit simulations and modeling exercises. Various BMS architectures—centralized, modular, and distributed—along with relevant safety protocols and industry standards were discussed.

Conclusion

The training provided an in-depth blend of theoretical knowledge and practical exposure to the core components of electric mobility. Complementing the technical sessions, an industrial visit to Tata Power-DDL’s Smart Grid Lab in Rohini on 18th July 2025 provided practical insights into smart energy systems and their integration with EV charging infrastructure. A total of 19 students from various engineering branches participated in the training, gaining both theoretical insights and hands-on experience through practical sessions, component integration projects, and simulation-based learning. Overall, the program significantly enhanced students’ understanding of electric vehicle technology and battery management systems, and sparked a deeper interest in pursuing opportunities within the EV domain.

 

Data Science & Data Analytics (DSDA)

Subject Code: ES 361

Class: B.Tech. III Year

Trainers: Mr. Harshit Gaur & Ms. Simran Kaur

Duration: 23rd June 2025 – 25th July 2025

1. Objective

The training program aimed to provide an integrated understanding of Data Science and Data Analytics, with practical exposure to tools such as Power BI, Python, TensorFlow, and Keras. It was designed to prepare students for real-world data handling, visualization, and machine learning tasks.

2. Program Structure

A. Data Analytics Module

Key topics covered:

  • Introduction to Data Analytics: Types of analytics, data lifecycle, and transformation.
  • SQL for Data Handling: CRUD operations, joins, filtering, and aggregation.
  • Power BI: Setup, data transformation, data modeling, DAX, visualizations, and report publishing.
  • Dashboard Storytelling: Students created dashboards from real-world datasets and presented insights.

Dates: 23rd June – 7th July 2025

Highlights:

  • Hands-on SQL and Power BI practice.
  • Focus on business intelligence and storytelling through visuals.
  • Final dashboard presentations by students to showcase learning.

B. Data Science Module

Key topics included:

  • Python for ML: Basics, lambda functions, scikit-learn introduction.
  • ML Algorithms: KNN, Random Forest, feature scaling, and cross-validation.
  • Natural Language Processing (NLP): NLTK, TF-IDF, sentiment analysis.
  • Deep Learning: Introduction to neural networks, CNNs, TensorFlow & Keras.
  • Capstone Projects:
    • Text Generation using NLP
    • Generative AI-based applications
    • CNN-based image analysis project

Dates: 8th July – 25th July 2025

Highlights:

  • Hands-on model building using Keras and TensorFlow.
  • End-to-end project implementations.
  • Real-world dataset use in project-based learning.

3. Methodology

  • Theory + Practice: Each topic was taught using 2 hours of lecture followed by 2 hours of lab work.
  • Interactive Sessions: Incorporated live demos, coding exercises, and group discussions.
  • Resource Sharing: Students accessed curated reading materials, YouTube tutorials, cheat sheets, and official documentation.
  • Outcome-Oriented: Each module concluded with mini-projects and presentations to assess understanding.

4. Learning Outcomes

By the end of the program, students were able to:

  • Understand the full data pipeline from ingestion to visualization.
  • Create impactful dashboards using Power BI.
  • Build machine learning and deep learning models.
  • Apply data analytics to business problems and real-world datasets.
  • Work collaboratively on data-centric projects and present findings.

5. Conclusion

The Summer Training in DSDA was a well-structured, hands-on learning experience that bridged theoretical knowledge with industrial tools and practices. The integration of analytics with data science, supported by practical assignments and project work, ensured a holistic learning journey for the students. It significantly enhanced their readiness for careers in data science, business intelligence, and AI.

 

Data Structures and Algorithms (DSA)

In Collaboration with Coding Ninjas Pvt. Ltd.

Coordinators: Dr. Ajay Dureja & Dr. Shelya

Training Duration: 23rd June 2025 – 25th July 2025

Training Topic: Data Structures and Algorithms (DSA)

Subject Code: ES 361

Class: B.Tech. III Year

Course Overview

Data Structures and Algorithms (DSA) form the cornerstone of efficient programming and software development. A strong grasp of DSA enhances problem-solving abilities and logical thinking—critical skills for addressing real-world challenges in the tech industry.

Objectives of the Training

The program was designed to benefit both beginners and advanced learners, strengthening their core programming concepts. It prepared students for coding competitions, technical interviews, and real-world software development.

Skills Acquired

  • Problem-Solving Techniques
  • Object-Oriented Programming (OOP)
  • Arrays and Linked Lists
  • Trees and Graphs
  • Dynamic Programming

Conclusion

By the end of the course, participants developed:

  • Proficiency in writing efficient and optimized code
  • A deeper understanding of algorithmic thinking
  • Awareness of memory management and performance optimization

This training empowered students to become confident problem solvers and future-ready developers.

 

MERN

Organized By: Brain Mentors Pvt. Ltd.

In Association with: Bharati Vidyapeeth’s College of Engineering (BVP), New Delhi

Training Duration: 23rd June 2025 – 25th July 2025

Training Topic: Full Stack Development using MERN + AI Integration

Subject Code: ES 361

Class: B.Tech. III Year

Course Overview

This summer training program was conducted to equip engineering students with hands-on skills in MERN Stack (MongoDB, Express.js, React.js, Node.js) and AI Integration, enabling them to build real-world full-stack applications enhanced by artificial intelligence.

Objectives of the Training

  • Build responsive web apps using MERN stack
  • Understand frontend-backend architecture
  • Create and consume REST APIs
  • Integrate Python-based AI models with Node/React
  • Host projects using cloud platforms (Vercel, Render, AWS)
  • Collaborate using Git & GitHub

Skills Acquired

  • Full Stack Web Development
  • React & Node.js Integration
  • MongoDB Modeling
  • RESTful API design
  • AI Integration with Python
  • Git & GitHub Workflow
  • Cloud Deployment and CI/CD Basics

Conclusion

This 5-week summer training organized by Brain Mentors provided a project-driven, real-world learning experience for B.Tech students of BVP. Students not only strengthened their MERN stack foundations but also gained hands-on exposure to integrating AI modules into full-stack applications—making them industry-ready for future placements and internships.

 

Cyber Security in Linux

Class: B.Tech. III Year

Duration: 23rd June 2025 – 26th July 2025

Trainers: Mr. Mohit Tiwari & Ms. Amrita Ticku

Objective

The training program aimed to provide students with a comprehensive understanding of Linux system administration, networking fundamentals, and cybersecurity practices. The objective was to equip participants with the skills required to secure systems, analyze vulnerabilities, and implement practical security measures using Linux-based environments.

Program Structure

The program was delivered in a modular 5-week format, combining theory with extensive hands-on practice.

  • Networking Fundamentals: TCP/IP, addressing, services, and configuration.
  • System Administration: Superuser privileges, process control, booting, and shutdown.
  • Linux Installation & Configuration: File systems, partitions, loaders, and package management.
  • User & Group Management: Authentication, permissions, and environment setup.
  • Linux File System & Processes: Mounting, inode structures, and automation.
  • Troubleshooting: Boot issues, GRUB/LILO problems, system logs.
  • Cyber Security Core:
    • Reconnaissance and information gathering
    • DNS, SSL/TLS, hashing & encryption techniques
    • Virtual machines and secure system configuration

Methodology

  • Blended Learning: Conceptual lectures followed by lab-based practice.
  • Hands-on Training: Real-world simulations using Linux terminals, virtual machines, and penetration testing tools.
  • Problem-Solving Approach: Troubleshooting scenarios and security use cases.
  • Collaborative Learning: Group tasks to configure secure systems and analyze vulnerabilities.

Learning Outcomes

  • Configure and manage Linux systems effectively.
  • Perform basic to advanced network and system troubleshooting.
  • Understand and apply cybersecurity fundamentals for securing Linux environments.
  • Identify, analyze, and mitigate common web application vulnerabilities.
  • Deploy and test encryption, hashing, and authentication mechanisms.
  • Work with virtualized environments to simulate real-world security challenges.

Conclusion

The Summer Training on Cyber Security in Linux provided participants with both foundational system administration skills and applied security knowledge. The combination of Linux internals, networking concepts, and cybersecurity practices offered a holistic learning experience. Students gained hands-on expertise in identifying threats, mitigating vulnerabilities, and implementing secure configurations skills highly relevant to today’s cybersecurity landscape. This training significantly strengthened their technical competencies, preparing them for future roles in system administration, cybersecurity analysis, and ethical hacking.

Training 2024

Training 2024

Following trainings are conducted in 2024

  1. Data Science by Veeyo Tech
  2. AI ML DL using Python
  3. MERN Stack
  4. Internet of Things using Arduino & NodeMCU
  5. Cyber Security in Linux
  6. Data Structure & Algorithm

MERN Stack

The Summer Training program on MERN Stack, conducted in July-Aug 2024, was organized by Brain Mentors Pvt Ltd. The program aimed to equip B.Tech students with practical knowledge and skills in web development using the MERN stack—comprising MongoDB, Express.js, React.js, and Node.js.

Data Structure & Algorithm

Data structures and algorithms are fundamental concepts in computer science and programming. They play a crucial role in designing efficient and effective software solutions.

Data Structure & Algorithm training course is done in association with Coding Ninjas Pvt. limited. The training program’s syllabus includes Problem Solving Techniques, Object Oriented Programming, Linear Data Structures, Trees, Advanced Data Structures & Dynamic Programming. This training helps students to attain novice and advanced programmer’s alike knowledge, so that they can dominate the algorithms and data structures necessary to do well in contests and to gain a competitive edge over other candidates in software interviews and to have programming skills to the next level.

Cyber Security in Linux

Summer Training in Cyber Security with Linux is a modular 5-week course, and includes practical (hands on) training. The course curriculum of Summer Training in Cyber Security with Linux comprises.

Internet of Things using Arduino & NodeMCU

The topics which were covered during the training include: Introduction to basic electronic component, Basics of C Programming, Introduction to Arduino, Introduction to IoT, Arduino Programming Basics, Sensors and Actuators, Intermediate Arduino Programming, Serial Communication, Advanced Concepts of IoT with Arduino, Front-end Techniques for IOT Interface, Security Threats in Internet of Things, Connecting Arduino to cloud platforms.

Data Science by Veeyo Tech

 The “Data Science with Python” course was designed to equip students with the essential skills and knowledge required for effective data analysis and decision-making in a business context. Over the span of five weeks, participants gained proficiency in Python programming, learned data manipulation using libraries like NumPy and Pandas, mastered data visualization with Matplotlib and Seaborn, delved into statistical analysis, and grasped fundamental machine learning concepts. They also explored tools such as Excel and Tableau to enhance their data analysis and visualization capabilities. By the end of the course, students were well-prepared to extract valuable insights from data and contribute to informed business decisions.

AI ML DL using Python

AI Engineer focuses on developing advanced AI algorithms and neural network architectures. Their duties include collecting data, refining machine learning (ML) models, and integrating AI into applications. Continuous upskilling through artificial intelligence courses is advised. The content of AI-ML course includes following modules.

Training 2023

Bharati Vidyapeeth’s College of Engineering, New Delhi organizes In-house training that focuses on areas that are currently in demand in the industry and help students to be better prepared in addition to the regular curriculum.

Summer Training/ Internship is mandatory part of an Engineering (B. Tech.) Student’s curriculum in most of the Colleges and Universities in India. This Summer Training is usually a 4 – 6 week or 45 days training during summer break between B. Tech. 2nd year and 3rd year.

Training/ Internships helps engineering students to acquire in-demand technical skills, facilitate self-growth, learn new complex abilities and aptitudes, and learn professional ethics. Internships provide opportunities to develop skills, build experience and practice applying knowledge in real-world settings. Internship provide students the right platform for testing your efficiencies.

In the last few years following trainings have been organized:

  • Android Tech
  • Automation in Electrical & Electronic Design
  • Cyber Security
  • Embedded System & Application
  • Machine Learning
  • ML & AI
  • Competitive Programming
  • Foundation C++
  • Foundation Java
  • Foundation Python
  • Machine learning and Deep learning
  • Cloud Computing
  • Foundation Courses
  • Android App Development
  • MERN stack and flutter
  • Implementation of AI in NCS

In the year 2022, following training were organized for students.

MERN Stack (MongoDb, ExpressJS, ReactJS, NodeJS)

MERN Stack is used extensively in mobile and web application development. As a result, numerous companies need developers with a working knowledge of this technology. Training on MERN stack is done in collaboration with Brain Mentors Private limited. Expert professionals from Brain Mentors lead MERN stack course that can help students understand this dynamic integrated system from basic skill level.

MERN stack in collaboration with Brain Mentors Private limited

Competitive Programming with Advanced Data Structure and Algorithm (DSA)

Competitive Programming with Advanced DSA training course is done in association with Brain Mentors Private limited. The training program’s syllabus includes Programming Abstraction and Data Structures – ADI (Algorithms Design and Implementation). This training helps students to attain novice and advanced programmers alike knowledge, so that they can dominate the algorithms and data structures necessary to do well in contests and to gain a competitive edge over other candidates in software interviews and to have programming skills to the next level.

Competitive programming and advanced DSA in collaboration with Brain Mentors Private limited

Cyber Security with Linux

Cyber Security with Linux: Summer Training in Cyber Security with Linux is a modular 5-week course, and includes practical (hands on) training. The course curriculum of Summer Training in Cyber Security with Linux comprises: Basic Networking, System Administration Overview, UNIX, Linux and Open Source, Duties of the System Administrator: Booting and Shutting Down Linux, Boot Sequence and Managing Software and Devices. Linux implements various aspects of security that are intended to complement each other. Instead of looking at anti-malware or firewalls, Linux kind of recognizes that permissions solve most of the issues in cyber security.

Students Trained in Cyber Security with Linux

Machine Learning and Deep Learning

This program “Machine Learning and Deep Learning” program is a valuable resource for beginners and experts. This Training inclusive of total 6 modules as Data Analysis and Visualization with Python, Data Preprocessing, Machine Learning, Natural Language Processing, Deep Learning and Project. This program will introduce students to Python, Machine Learning, Deep Learning etc. from Basics to Advance. 

Electric Vehicles Integrated with Renewable Energy Sources

Electric Vehicles Integrated with Renewable Energy Sources training is done in association with Vision Automation Solutions. This training has four modules which includes Basic Electrical, Renewable Energy-Solar PV, Electrical Vehicle and Project and Industrial Visit. This training help the students by giving working knowledge of industry environment and their role in near future. During training students had an industrial visit to Fab Tech-panel Fabrication Company, Greater Noida for exposer of industrial environment.