Technology Consultant | AI | Data Science | Machine Learning

Industrial Internship and Training Program 2024

Generative AI and Prompt Engineering| Data Science and Machine Learning | Artificial Intelligence | Data Analytics using Python | Cloud Infrastructure and Computing.

About Program

Our Industrial Internship and Training Programs are designed to bridge the gap between academic learning and industry demands by providing undergraduate engineering students with hands-on experience and practical skills in cutting-edge technologies. These programs focus on Data Science and Machine Learning, Internet of Things (IoT), and Generative AI and Prompt Engineering. By participating in these programs, students gain valuable insights and real-world experience, preparing them for successful careers in the tech industry.


Our programs offer an immersive, on-campus, offline learning experience tailored to equip students with the knowledge and skills needed to excel in today’s technology-driven world. Each program is carefully structured to provide a blend of theoretical knowledge and practical application, ensuring that students not only understand the concepts but also learn how to implement them in real-world scenarios. With a focus on industry-relevant skills, our programs help students transition smoothly from academia to professional life.

Why do students need to join this Internship?

  • Hands-on Learning Experience: Learners will gain practical experience and skills through hands-on exercises and projects during the program.
  • Learning from Industry Experts: Expertise of the instructors leading the Training and Internship, including their industry experience and knowledge in the subject matter.
  • Industry Level Content: Training content to current industry trends and emerging technologies, highlighting the value of learning new and in-demand skills.
  • Opportunities to connect with Industry: Chance for participants to connect with peers, industry professionals, and potential mentors during the Training and Internship, fostering valuable networking opportunities.
  • Internship Certification: participants to receive a certification upon successful completion of the Project/Internship, adding value to their resume and professional development.
  • Practical Applications: Showcase how the skills and knowledge gained from the Training and Internship can be applied to real-world scenarios and projects, demonstrating the immediate value of participation.


Prerequisites:  Any graduating candidate can join

Certificate: Certificate of Internship

ToC for Internship & Training:

Module -1 – Introduction to Course

  • Introduction to Data Analytics
  • Real time use and application 
  • Career opportunities 

Module 2 – Python Programming Basics 

  • Getting started with Python
  • What is Python?
  • Installing Anaconda
  • Variables, and Data Structure
  • List, tuples and dictionary
  • Control Structure
  • Functions in python
  • Lambda functions
  • Object Oriented Programming Modules
  • Using Packages Os package, time and datetime
  • File Handling in Python
  • Miscellaneous Functions in python 

Module 3 – Statistics for Data Science 

  • Introduction to Statistics
  • Population and Sample
  • Descriptive Statistics v/s Inferential Statistics
  • Types of variable
  • Categorical and Continuous Data
  • Ratio and Interval
  • Nominal and Ordinal Data
  • Descriptive Statistics
  • Measure of Central Tendency – Mean, Mode and Median
  • Percentile and Quartile
  • Measure of Spread – IQR, Variance and Standard Deviation
  • Coefficient of Variation
  • Measure of Shape – Kurtosis and Skewness
  • Correlation Analysis
  • Inferential Statistics
  • Empirical Rule & Chebyshev’s Theorem
  • Z Test
  • One Sample T test, independent t test
  • ANOVA – f test
  • Chi Square test

Module 4 – Working with numpy & Pandas 

  • Working with Numpy
  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Accessing Array
  • Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
  • Shape Manipulation & Broadcasting
  • Linear Algebra using numpy
  • Stacking and resizing the array
  • random numbers using numpy
  • Working with Pandas
  • Data Structures
  • Series, DataFrame & Panel
  • DataFrame basic properties
  • Importing excel sheets, csv files, executing sql queries
  • Importing and exporting json files
  • Data Selection and Filtering
  • Selection of columns and rows
  • Filtering Dataframes
  • Filtering – AND operaton and OR operation

Module 5 – Working with numpy and Pandas

  • Working with Pandas
  • Data Cleaning
  • Handling Duplicates
  • Handling unusual values
  • handling missing values
  • Finding unique values
  • Descriptive Analysis with pandas
  • Creating new features
  • Creating new categorical features from continuous variable
  • combining multiple dataframes
  • groupby operations
  • groupby statistical Analysis
  • Apply method
  • String Manipulation 

Module 6 – Data Visualization

  • Basic Visualization with matplotlib
  • Matplotlib Features
  • Line Properties
  • Plot with (x, y)
  • Controlling Line Patterns and Colors
  • Set Axis, Labels, and Legend Properties
  • Alpha and Annotation
  • Multiple PlotsSubplots
  • Advance visualization using seaborn
  • Types of Plots and Seaborn
  • Boxplots
  • Distribution Plots
  • Countplots
  • Heatmaps
  • Voilin plots
  • Swarmplots and pointplots

Module 7 – Capstone Project

  • Data Science Standard Project
  • Data Science Project Life cycle
  • Project Topic
  • Data Capturing
  • Data Cleaning 
  • Data Analytics
  • Working on tools
  • Data Visualization tools
  • Project Report Completion

Let’s talk to us if you have any query

Pre-requisites: Basic Understanding of Python

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1 – Linear Regression

  • The conceptual idea of linear regression
  • Predictive Equation
  • Cost function formation
  • Gradient Descent Algorithm
  • OLS approach for Linear Regression
  • Multivariate Regression Model
  • Correlation Analysis – Analyzing the dependence of variables
  • Apply Data Transformations
  • Overfitting 
  • L1 & L2 Regularization
  • Identify Multicollinearity in Data Treatment on Data
  • Identify Heteroscedasticity Modelling of Data
  • Variable Significance Identification
  • Model Significance Test
  • R2, MAPE, RMSE
  • Project: Predictive Analysis using Linear Regression 


Module 2 – Logistic Regression

  • Classification Problem Analysis
  • Variable and Model Significance
  • Sigmoid Function
  • Cost Function Formation
  • Mathematical Modelling 
  • Model Parameter Significance Evaluation
  • Implementing logistic regression using sklearn
  • Performance analysis for classification problem
  • Confusion Matrix Analysis
  • Accuracy, recall, precision and F1 Score
  • Specificity and Sensitivity
  • Drawing the ROC Curve
  • AUC for ROC 
  • Classification Report Analysis
  • Estimating the Classification Model
  • Project: Predictive Analysis using Logistic Regression


Module 3 – KNN & Decision Tree K Nearest Neighbour

  • Understanding the KNN
  • Distance metrics
  • KNN for Regression & classification
  • Implementing KNN using Python
  • Case Study on KNN
  • Handling overfitting and underfitting with KNN
  • Decision Tree
  • Forming Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Entropy Approach
  • Gini Entropy Approach
  • Variance – Decision Tree for Regression
  • Decision Tree Evaluation
  • Overfitting of Decision Tree
  • Handling overfitting using hyperparameters
  • Hyperparameters tuning using gridsearch
  • VIsualizing Decision Tree using graphviz


Module 4 – SVM & Ensemble Learning

  • Support Vector Machines
  • Concept and Working Principle
  • Mathematical Modelling
  • Optimization Function Formation
  • Slack Variable
  • The Kernel Method and Nonlinear Hyperplanes
  • Use Cases
  • Programming SVM using Python
  • Project – Character recognition using SVM


Module 5 – Ensemble Learning

  • Concept of Ensemble Learning 
  • Bagging and Boosting
  • Bagging – Random Forest
  • Random Forest for Classification
  • Random Forest for Regression
  • Boosting – Gradient Boosting Trees
  • Boosting – Adaboost
  • Boosting – XGBoost
  • Stacking


Module 6 – Unsupervised Learning Clustering 

  • Application of clustering
  • Hierarchical Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
  • Customer segmentation using KMeans
  • Cluster Size Optimization vs Definition Optimization
  • Projects & Case Studies
  • Dimensionality Reduction – PCA
  • Dimensionality Reduction, Data Compression
  • Curse of dimensionality
  • Multicollinearity
  • Factor Analysis
  • Concept and Mathematical modelling
  • Use Cases


Module 7 –Capstone Project

  • Working Final Project
  • Splitting final Project into phases
  • Working on structuring project
  • Do’s and Don’ts with Machine Learning

Let’s talk to us if you have any query


  • Working knowledge of Python programming
  • Basic understanding of Machine Learning algorithms
  • Understanding of working with Data
  • Linear Algebra and Matrix calculations

Certificate: Certificate of Internship

ToC for Internship & Training:

Module – 1 – Overview of AI, ML & Deep Learning

  • Artificial Intelligence & Machine Learning Introduction
  • Computer Vision Overview
  • Applications of Computer Vision
  • Deep Learning Introduction
  • Deep learning v/s Machine Learning
  • GPU for Deep Learning
  • Applications of Deep Learning
  • Algorithms and use cases in Deep Learning


Module 2 – Artificial Neural Networks

  • Introduction to Neural Networks
  • Working of Neural Networks
  • Mathematical modelling of Neural Networks
  • Architectures of ANN
  • ANN learning process
  • Gradient Descent Algorithm
  • Cost Function formation
  • Python programming example
  • Softmax Classifier & ReLU Classifier
  • Dropout Optimization
  • Mathematics behind backpropagation
  • Deep Neural Networks using Tensorflow
  • Activation Functions for Neural Networks
  • Optimization Techniques – SGD, ADAM, LBFGS
  • Controlling Overfitting
  • Regularization
  • Momentum in Neural Networks
  • Neural Network Tuning and Performance Optimization


Module 3 – Working with Tensorflow & Pytorch

  • Introduction to TensorFlow & keras
  • The Programming Model
  • Data Model, Tensor Board
  • Programming Neural Networks with Tensorflow
  • Working with Pytorch
  • Basics of Pytorch
  • Programming model of Pytorch
  • Building Linear Regression and Logistic Regression with Pytorch
  • Examples and use cases


Module 4 – Text and Speech pre-processing

  • Working with speech to text and text to speech conversions
  • Text pre-processing with Regular Expressions
  • Working with NLTK
  • Tokenization, stemming, lemmatization
  • PoS Tagging
  • Spelling correction
  • Working with spaCy
  • NER
  • Dependency Parsing
  • Document Similarity Matching


Module 5 – Text Classification

  • Text Classification – Vectorization Techniques
  • Tf-IDF, count vector co-occurence Vectorization
  • N-gram feature modelling
  • Feature Pruning methods
  • Project: Sentiment Analysis
  • Project: Building an Email Classification Model


Module 6 – Social Media Analytics

  • Collecting live tweets from twitter
  • Cleaning and processing tweets
  • Information extraction from tweets
  • Converting unstructured data to semi structured
  • Analysing tweet data
  • Project: Streaming live tweets and Sentiment Analysis
  • Wordcloud


Module 7 – RNN and LSTM

  • Recurrent Neural Networks
  • Basic concepts of RNN
  • Unfolding Recurrent Neural Networks
  • The Vanishing Gradient Problem
  • The Exploding Gradient Problem
  • LSTM Networks
  • Understanding of LSTM
  • Erase Gate, Input Gate and Read Gate
  • Overview of GRU
  • Case study
  • Implementing LSTM using Tensorflow-keras
  • Implementing LSTM using Pytorch
  • Text Classification using NLP
  • Developing a chatbot application using NLP


Module 8 – Transfer Learning for NLP

  • Understanding the concept of Transfer Learning for NLP
  • General issues with NLP problems
  • Ideas for transfer learning approach for different situations in case of NLP 
  • Using Universal Sentence Encoders
  • Deep Averaging Networks
  • Sequence to Sequence Models
  • Encoder and Decoder Understanding
  • Attention-based models
  • Transformer Models – Self Attention models
  • Using ELMo for word vectorization
  • Working of BERT
  • Working of GPT-2
  • Working of XLNet
  • Using BERT, GPT-2, XLNet for text classification


Module 9 – Image Processing

  • Getting started with Images
  • Image Processing with OpenCV
  • Image Acquisition and manipulation using OpenCV
  • Video Processing
  • Edge Detection
  • Corner Detection
  • Color Detection
  • Face Detection
  • Image Scaling for ANN
  • Face Detection in an image frame
  • Object detection


Module 10 – Convolution Neural

  • Convolutional Neural Networks
  • CNN Architecture
  • The Neuroscientific Basis for Convolutional Networks
  • Convolution Process
  • MaxPooling, dropout
  • Maths behind CNNs
  • Feature Extraction
  • Variants of the Basic Convolution Function
  • Efficient Convolution Algorithms
  • Variety of Convolutional Networks
  • Implementing CNNs using Keras
  • MNIST Data – Digit Classification using CNN
  • Fashion Product Identification using CNN
  • CNN implementation example using Pytorch


Module 11 – Transfer Learning for Computer Vision

  • Understanding the concept of Transfer Learning
  • Ideas for transfer learning approach for different situations
  • Overview of imagenet datasets and pre-trained models – VGG16, VGG19, ResNet50, MobileNet
  • Implementing Transfer Learning-based Image Classifier using ResNet50
  • Optimizing Transfer Learning-based models
  • Using other pre-trained models from Tensorflow hub for Computer Vision applications
  • Video Processing using Neural Networks


Module 12 – Object Localization and Object Detection

  • Object Localization Overview
  • Image augmentation
  • Bounding box augmentation
  • Object Localization using Transfer Learning
  • Object Detection Overview
  • Region based CNN
  • Fast RCNN
  • Faster RCNN
  • Implementing RCNN using Tensorflow
  • Introduction to SSD
  • Working of SSD
  • Implementation of SSD using tensorflow


Module 13 – Image Segmentation

  • Image segmentation introduction
  • Encoder Decoder approach for Image segmentation
  • Masked RCNN
  • Working with Unet – Encoder Decoder approach
  • Implementation with Python


Module 14 – Capstone Project

  • Working Final Project
  • Splitting final Project into phases
  • Working on structuring project
  • Summary

Let’s talk to us if you have any query


  • Basic of Networking
  • Absolute Beginners. No prior AWS experience is necessary
  • Interested in Deploying Applications on AWS
  • Cloud Computing Enthusiasts
  • AWS System Requirements- Need to set up an AWS Account(Most of the Services come with AWS free tier eligibility)
  • Windows: Putty, Putty KeyGen, and Web Browser with Internet connectivity.
  • Linux/Mac: Default Terminal

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1: Introduction to Cloud Computing

  • Introduction to cloud computing
  • Essential Characteristics of Cloud Computing
  • Service Models in Cloud computing
  • Deployment models in Cloud Computing
  • Introduction to AWS
  • AWS Account creation &free tier limitations overview

Module 2: Identity Access Management

  • Root Account Vs IAM user
  • Multi-Factor Authentication for Users
  • IAM Password Policies
  • Creating Customer Managed Policies.
  • Groups
  • Roles

Module 3: Glacier Storage

  • What is Simple Storage Service (S3)
  • Storage Classes
  • Versioning
  • Cross-region replication
  • Life Cycle Management
  • Security & Encryption
  • Static Webhosting with S3 bucket
  • Events configuration on S3 buckets
  • Enabling cross-account access for S3
  • S3 Data management and backup using 3rd Party applications.
  • S3 Cross-Account Access and Pre-Signed URLs
  • Storage Gateway

Module 4: Linux Introduction

  • Basics of Linux for AWS
  • Linux Installation and Basic commands overview
  • Web Server and Services Configurations
  • Compute
  • EC2 Instance Launch Wizard
  • EC2 Instance Types
  • Generating custom Public Key and Private keys for EC2 instances
  • Security groups
  • Volumes and Snapshots
  • Creating customized Amazon Machine Images
  • RAID Overview and RAID Configurations
  • User Data and Metadata
  • ElasticLoad Balancers & Health Checks
  • Auto Scaling Groups
  • CloudWatch
  • Creating Billing Alarm and EC2 instance alarms.
  • AWS CLI&EC2 Roles
  • Elastic File System
  • AWS Lightsail
  • Elastic Beanstalk
  • Placement Groups

Module 5: Logging and monitoring in Amazon S3

  • Amazon CloudWatch Alarms
  • AWS CloudTrail Logs
  • Amazon S3 Access Logs
  • AWS Trusted Advisor
  • Logging configuration of Amazon S3 buckets.
  • Security checks for Amazon S3 buckets that have open access permissions.
  • Fault tolerance checks for Amazon S3 buckets that don’t have versioning enabled, or have versioning suspended.

Module 6: Okta tool for Identity and Access Management

  • Step 1: Enable provisioning in AWS SSO
  • Step 2: Configure provisioning in Okta
  • Step 3: Assign access for users and groups in Okta
  • (Optional) Step 4: Configure user attributes in Okta for access control in AWS SSO
  • (Optional) Passing attributes for access control
  • Troubleshooting

Module 7: Route 53

  • DNS Records overview
  • Routing Policies
  • Hosting sample Website and configuring Policies
  • Simple Routing Policy
  • Latency Routing Policy
  • Failover Routing Policy
  • Weighted Routing Policy
  • Geolocation Routing Policy.

Module 8: Database

  • Launching a RDS  Instances (MySQL, MSSQL & Aurora)
  • Multi-AZ & Read Replicas for RDS instances
  • DynamoDB
  • Redshift
  • Elastichache
  • Database Migration Service and Schema conversion tool

Module 9: VPC (Virtual Private Cloud)

  • Networking Basics
  • Creating custom VPCs and custom Subnets
  • Network ACL’s
  • Route Tables & IGW
  • VPC Peering
  • Flow log creation
  • VPN Configuration with AWS (OpenVPN)

Module 10: Security Options

  • CloudTrail
  • AWS Config
  • Key Management Services
  • AWS Certificate Manager
  • AWS Inspector
  • AWS Trusted Advisor
  • Content Delivery Networks / CloudFront

Module 11: Application Services

  • Simple Email Service
  • Simple Queue Service
  • Simple Workflow Service
  • Simple Notification Service
  • SMS – Server Migration Service
  • Migrating server from on-premises to cloud
  • Cloud Formation
  • Directory Services and Adding EC2 instance to Domain
  • AWS TCO Calculator and Simple Monthly calculator

Module 12: Project Work

  • Working Final Project
  • Splitting final Project into phases  Working on structuring porject
  • Do’s and Don’ts with Machine Learning  Productization of Machine Learning
  • Application

Let’s talk to us if you have any query

Prerequisites: Basic of Linux and Networking, any graduating candidate can join

Certificate: Certificate of Internship

ToC for Internship & Training:

Unit 1: Introduction to ethical hacking

Information security controls

Information security threats and attack vectors

  • Top information security attack vectors
  • Motives, goals, and objectives of information security attacks
  • Information security threats
  • Information warfare
  • Ipv6 security threats

Hacking concepts

Hacking phases

Types of attacks

  • Types of attacks on a system
  • Operating system attacks
  • Miss configuration attacks
  • Application-level attacks

Unit 2: foot printings and reconnaissance

Foot printing concepts

  • Foot printing terminology
  • What is foot printing?
  • Why foot printing?
  • Objectives of foot printing

Foot printing threats

Website foot printing

  • Mirroring entire website
  • Website mirroring tools
  • Monitoring web updates using website watcher

Email foot printing

  • Tracking email communications
  • Collecting information from email header
  • Email tracking tools
  • Company’s plans?

Foot printing using google

  • Footprint using google hacking techniques
  • What a hacker can do with google hacking?

Whois foot printing

Dns foot printing

Network foot printing

Unit 3: System Hacking

  • Information at hand before system hacking stage
  • System hacking: goals
  • Hacking methodology (chm)
  • System hacking steps
  • Types of password attacks
  • Offline attack: rainbow attacks
  • Default passwords
  • Microsoft authentication
  • Password cracking tools
  • Escalating privileges
  • Key logger
  • Spyware

Unit 4: Trojans and Backdoors

  • Trojan concepts
  • Trojan infection
  • Types of trojans
  • Trojan detection
  • Anti-trojan software

Unit 5: SQL Injection

  • Sql injection concepts
  • Types of sql injection
  • Blind sql injection
  • Advanced sql injection
  • Sql injection tools
  • Evasion techniques
  • Counter-measures

Unit 6: Penetration Testing

  • Pen testing concepts
  • Types of pen testing
  • Pen testing techniques
  • Pen testing phases
  • Pen testing roadmap
  • Outsourcing pen testing services

Prerequisites: None, any graduating candidate can join

Hardware Kit: Attendee can buy the hardware kit from market or from us.

Certificate: Certificate of Internship

ToC for Internship & Training:

Module 1- Introduction to IoT

  • Introduction to Internet of Things
  • M2M towards IoT -the global context
  • Scope of IoT – Smart home, Smart Grid Applications
  • IoT in India – Reality v/s Hype
  • IoT Job Market
  • Skills required to switch career to IoT
  • Industries working on IoT
  • IoT Products by Indian Companies
  • Internet of Things in Indian Universities Curriculum
  • Applications of IoT Electrical Engineering
  • IoT Standards in Industry
  • IoT Hardware Requirements
  • Analysis of Arduino Uno, Arduino Yun, Raspberry Pi, Beaglebone Black, Intel Edision & Galileo

Module 2 -Hardware Layer – Arduino

  • Industrial Internet of Things
  • Working with Smart Grid – Analysis
  • Getting started with Arduino Uno R3
  • Basics of AVR MCU – RAM, Flash Memory and timers
  • Arduino – Opensource Hardware Platform
  • Pin Configuration and functionalities
  • Getting started with Arduino IDE
  • LED Interfacing with Arduino
  • Introduction to Serial Communication
  • PC Controlled Communication
  • Introduction to basic sensors
  • Sensor 1: Working & Interfacing of IR Proximity Sensor


Module 3 -Hardware Layer – Sensor Interfacing

  • Sensor 2: Working & Interfacing of MQ2 Sensor
  • Sensor 3: Ultrasonic Sensor Interfacing (demo)
  • Sensor 4 & 5: DHT11 Interfacing, working principle
  • Measuring temperature & Humidity using DHT11
  • Analyzing sensor data on Serial Monitor & Serial Plotter
  • Selecting a sensor for your use case
  • Commercial/Industrial/Military/Medical/Food grade sensors
  • Automatic Street Light Management for Smart Cities
  • Understanding Wastage of Electrical Energy due to street lights mismanagement.
  • Traffic Light Management Automation System


Module 4-Network Layer – Wireless Communication Protocols & Bluetooth

  • IPv4 Vs IPv6
  • Introduction to 6LowPAN
  • IoT Physical Layer Protocols
  • Getting started with HC05 – Bluetooth Module
  • Connecting HC05 with Arduino
  • Sensor Data Analytics using readily available Bluetooth Terminal Android Apps
  • Android Controlled Device Automation with Arduino
  • Working with Relay & Interfacing with Arduino
  • Controlling AC Appliances with from PC – SMART Home Applications


Module 5 -Network Layer – WiFi

  • Using Voice Recognition Technique
  • Sending voice to text from android app via Bluetooth to arduino
  • Voice controlled Device Automation
  • Wifi & Lifi
  • Getting Started with ESP8266-01
  • Configuration, Pin Layout and Applications
  • Testing AT Commands with ESP8266
  • Connecting to a network
  • HTTP Request Format
  • Making HTTP Local Webserver using ESP8266


Module 6 -ESP8266 & Thingspeak

  • Using ESP8266 as a HTTP Client
  • Uploading live sensor data on thingspeak cloud using ESP8266 & GET Request
  • Making a Local Webserver using Arduino
  • Using Arduino as a TCP data server
  • Accessing UI in a local network
  • Analyzing HTTP callbacks in webserver
  • Projects and Tasks
  • Introduction to Transport Layer Protocols


Module 7- IoT Application Layer Protocol

  • HTTP
  • MQTT
  • XMPP
  • CoAP
  • AMQP
  • Websockets


Module 8- Python Programming for IoT

  • Getting started with Python
  • Variables, and Data Structure
  • List, tuples and dictionary
  • Functions in python
  • Control Structure
  • Object Oriented Programming
  • Using Packages
  • Os, time and datetime
  • File Handling in Python
  • Miscellaneous Functions in python


Module 9 -Python Programming

  • Serial Communication in Python
  • Controlling Arduino using Python
  • Interfacing APIs with Python
  • MQTT with Python
  • Installing Paho
  • Publish data to a MQTT topic using Paho
  • Subscribing to an MQTT Topic using Paho


Module 10 -Raspberry Pi

  • Getting Started with Raspberry Pi
  • Installing OS in Rpi
  • Command line and GUI Interface
  • Raspbian OS Introduction & Tools
  • Interfacing GPIOs with LEDs
  • Interfacing sensors
  • Serial Communication
  • Controlling Arduino from raspberry pi


Module 11 -Raspberry Pi

  • Getting started with MQTT on Raspberry Pi
  • Controlling LED using Android MQTT Client
  • Getting sensor data using Android MQTT Client
  • Using Raspberry pi as HTTP Client to send live sensor data to thingspeak
  • Installing Mosquitto on Raspberry pi
  • Making pi a local MQTT broker
  • Testing Publish and subscribe model on RPi
  • Publishing data from PC, Android to RPi over a local network
  • Controlling Pi GPIOs using MQTT broker

Module 12 -IoT Gateway and Node-red

  • Designing the IoT Gateway system
  • Gathering data from multiple publishers
  • Making Raspberry Pi as a IoT Gateway
  • Analyzing sensor data in smartphone over internet
  • Introduction to Node-red and node.js
  • Getting started with node-red
  • Installing dashboard, ThingSpeak node and IBM Watson node
  • Basic flow in node-red
  • Connecting twitter with trigger switch
  • Twitting Sensor data on Twitter
  • Uploading Sensor data on Thingspeak using node-red
  • Uploading data to IBM Watson demo Platform using node-red
  • Controlling devices from Twitter and other cloud services via node-red
  • Creating front end Visualization using node-red


Module 13 -Socket Programming

  • Socket programming with python
  • TCP v/s UDP
  • Setting up TCP server & TCP client on Raspberry Pi using python code and socket Programming
  • Testing TCP client server relationship
  • Setting up UDP server and UDP client on RPi using Python code and socket programming
  • Testing UDP client server relationship
  • CoAP Protocol (Constrained Application Protocol)
  • Working of CoAP
  • HTTP v/s CoAP
  • Using CoAPthon library with python
  • Setting up a CoAP server using CoAPthon
  • Setting up CoAP client using CoAPthon

Module 14 – IoT Cloud

Module 15-Project Development


Let’s talk to us if you have any query

Eligibility & Procedure to join the Program

  1. Candidate must be pursuing any Technical Degree (i.e. B. Tech/B.E.)
  2. Duration the program: 2, 4 and 6 Weeks
  3. Candidate has to submit the project report by end of the program. 
  4. Candidate will get a certificate of internship the certificate can be customized as per the university format.
  5. Faculty members can host this program for their students on their college campus.

Request us to host Internship and Training for your students.

Dear Sir/Ma’am,

We are offering an on-campus Industrial Internship and Training program for UG (2nd and 3rd year) students.


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Frequently Asked Questions (FAQ's)

Who can attend?

Any Student pursuing Technical Degree( i. e. B. Tech/B.E.), 2nd year, 3rd year and 4th year students can attend the program.

Does student has to pay any fee to attend the Program?

We are charging only tuition fee, because we involve our Technical & Management resources to train the candidate and help them to works on projects.

Do I need to visit the Company office for any registration or Documentation?

No, registration is totally online process, We offer Internship and Training program in PAN India so we do provide the flexibility to the students for registration and attend the program online.

Is this Online or Offline Program?

We offer offline (on campus) and online program therefor students from PAN India get benefited from the Program.

How can I join the program?

  1. Candidate must be pursuing any Technical Degree (i.e. B. Tech/B.E.)
  2. After Completion of registration company will book your seat for particular Training and Internship batch
  3. We will share the joining link and provide the access to digital classroom
  4. During the program, the candidate will work on a real-time based followed by training. 
  5. Candidate has to submit the project report by end of the program. 
  6. Candidate will certificate of internship as given above, the certificate can be customized as per the university format.

Will I get any Certificate

The candidate will certificate of internship as given above, the certificate can be customized as per the university format.