-->

Course-Details


IOT

Module 1: Internet

  • An Overview: Introduction.
  • History of Internet.
  • Internet Technology.
  • Basics of Internet.
  • Classification of Internet.
  • Topologies.
  • Applications.
  • Internet of Things and Related Future Internet Technologies.
  • Internet of Things Vision.
  • Towards the IoT Universe(s).
  • The Internet of Things Today.
  • Lab: Familiarization of components.

Module 2: Communication Technologies

  • Internet Communication Technologies.
  • Networks and Communication.
  • Processes , Data Management.
  • IoT Related Standardization.
  • Protocol.
  • Communication protocols.
  • Types of communication protocols.
  • Addressing Schemes.
  • M2M Service Layer Standardization.
  • OGC Sensor Web for IoT.
  • IEEE and IETF.
  • ITU-T.
  • Current trends in Internet: Internet of everything.
  • Internet of everything.
  • Internet of things,.
  • Storage, Databases.
  • Lab: Interfacing Bluetooth, Wi Fi, Zigbee, GSM.

Module 3: Cloud Technology

  • Introduction.
  • Why cloud?
  • How to implement cloud?
  • Usage of cloud.
  • Scalable Computing.
  • Cloud computing.
  • Characteristics of cloud computing.
  • Classifications.
  • Virtual machines.
  • Virtualization technology.
  • Models of distributed and cloud computing.
  • Distributed computing.
  • Clustering.
  • Grid computing.
  • Service oriented Architecture.
  • Performance and Security.
  • Performance analysis.
  • Security.
  • Implementations of Cloud computing.
  • Lab: Python programming

Module 4: Internet of Things developments

  • IoT : An overview .
  • Characteristics.
  • IoT technology.
  • IoT as a Network of Networks.
  • IoT architecture.
  • IoT developments.
  • Smart Technology.
  • Brief introduction of smart technology.
  • Smart devices.
  • Smart environment.
  • IoT Components.
  • Basic Principles.
  • Embedded technology Vs IoT.
  • Sensors, Wireless sensor networks.
  • Lab: Aurdino (C coding), Rasberry Pi (Python).

Module 5: Big Data

  • Big Data.
  • BigData versus IoT.
  • BigData influcement in IoT.
  • A cyclic model of BigData.
  • Cloud and Internet of Things.
  • Data Storage.
  • Analysis and Communication.
  • Classifications.
  • Characteristics of BigData.
  • Types of BigData.
  • Analysing of Data.
  • Applications.
  • Real time situations.
  • BigData tools.
  • A combined application of IoT.
  • Cloud and BigData in IoT.
  • Lab: Python programming.


ARTIFICIAL INTELLIGENCE

Module 1: Introduction to AI

  • What is AI.
  • The foundation of AI.
  • History and applications.
  • Production systems.
  • Structures and strategies for state space search.
  • Informed and Uninformed searches.
  • Lab: Programming introduction.

Module 2: Search methods

  • Data driven and goal driven search.
  • Depth first and breadth first search.
  • DFS with iterative deepening.
  • Heuristic search-best first search.
  • A* algorithm.
  • AO* algorithm.
  • Constraint satisfaction.
  • Crypt Arithmetic problems.
  • Lab: Programming in Windows, Linux.

Module 3: Python

  • Basics Data Type.
  • Conditional Statements.
  • Looping, Control Statements.
  • String, List And Dictionary Manipulations.
  • Python Functions.
  • Modules and Packages.
  • Analysis and Communication.
  • Object Oriented Programming in Python.
  • Regular Expressions.
  • Exception Handling.
  • Introduction to Database Management System & SQL, Database Interaction in Python.
  • Data Analysis & visualization – using numpy, matplotlib, scipy.
  • Lab: Python.

Module 4: Machine learning & Deep learning

  • Supervised and Unsupervised Learning.
  • Classification and Regression.
  • Linear Regression.
  • KNN.
  • K Means.
  • Logistic Regression.
  • Support Vector Machines (SVM).
  • Decision Tree.
  • Naïve Bayes.
  • Ensemble Methods.
  • Random Forest.
  • Boosting and Optimization.
  • Deep Learning Concepts.
  • Basics of Artificial Neural Network.
  • Deep Neural Networks.
  • Convolutional Neural Network (CNN).
  • Recurrent Neural Network (RNN).
  • Tensorflow.
  • Keras.
  • Introduction to Generative Adversarial Networks(GAN).
  • OpenCV.
  • Lab: Machine learning and Deep learning in Python.

Module 5: Expert Systems

  • Rule based expert systems.
  • Natural language processing.
  • Natural language understanding problem.
  • Deconstructing language.
  • Syntax stochastic tools for language analysis.
  • Natural language applications.
  • Lab: NLP based Speech recognition.

We are an organisation engaged in providing Robotics and Technology based education to young minds through the schooling education system in India.

latest Courses

Certified Hacking Forensic Investigator

Bachelor in Computer Application

CCNA-CISCO Certified Network Associate

Bachelor in Computer Application

Newsletter

Be the first to get any news/updates from our team,

Please subscribe now.