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MACHINE LEARNING USING MIT App

The first AI unit- Introduction to machine learning: Image classification enables students to learn about the basics of machine learning and create their own apps that implement these concepts through image classification. It is aimed at College and University students and uses an MIT extension called the LookExtension that adds object recognition using a neural network to a mobile App.

The unit comprises two lessons. In the first students get a basic understanding of how machine learning for image classification works, using images of cats as an example, then  they go through the guided tutorial on Google’s Teachable Machine. If you are not familiar with this resource see our account Google’s Teachable Machine – What it really signifies for how it works and a discussion of what it means. In the second lesson students build their own machine learning app, Whatisit which has been trained for 999 different classes. They then test its capabilities by photographing new objects and using the LookExtension’s Classify button. Until recently Machine Learning has been an elite pursuit – done at postgraduate or post doctoral level. Now new tools allow those without such a background to join in.

INTERNET OF THINGS (IOT) ‎COURSE OBJECTIVE

With knowledge in IoT becoming a necessary skillset for data scientists, the Certification Program is ideal for professionals and freshers looking to enter the field of automated products and communication interfaces. Focusing on data capture, storage and data analytics, embedded electronics and client-server architecture, the course guides students to be adept at carving practical applications for modern day industries.

LEARNING OUTCOMES – INTERNET OF THINGS‎ (IOT) TRAINING PROGRAM

  • Be able to design independent IoT devices for sectors like retail, manufacturing and construction
  • Be able to comprehend IoT architecture and modern microcontrollers for data capture and signal relaying
  • Be able to understand the advantages of cloud storage
  • Be able to make hardware compatible MCUs
  • Be able to set up a HTTP server and able to deploy python modules for basic data analytics
  • Be able to understand IoT communication protocols

WHO SHOULD ATTEND INTERNET OF THINGS‎ (IOT) CERTIFICATION TRAINING

  • Candidates aspiring to be IoT Analysts
  • Analytics Managers / Professionals, IoT Experts, Software Developers
  • Candidates aspiring to get an overall understanding of “IoT Analytics”
  • Professionals who are looking to get understanding on IoT Architecture, Cloud IoT, IoT Systems Integration and IoT Implementation Strategy
  • Employees of organizations, who are planning to shift to IoT and Data Analytics
  • Finally – Students who are aiming to get an understanding to embark on the journey of IoT, Cloud Computing and Data Analytics

Overview of IoT Connectivity Methods and Technologies

Course Content

What is the Internet of Things (IoT)?

  • Concepts and definitions of The Internet of Things (IoT)
  • History  of IoT
  • Applications
  • IoT standards
  • Requirements
  • Functionalists and structure
  • IoT enabling technologies
  • IoT architecture
  • Major components of IoT
  • Hardware, sensors, systems-on-a-chip, firmware, device drivers, application software, connectivity, cloud and security
  • Role of wired and wireless communication
  • IoT communication and networking protocols
  • IoT services and applications
  • Big data and analytics
  • Security
  • Cloud computing and the Internet of Things
  • Semantic Web 3.0 Standard for M2M and IoT
  • IoT Platforms
  • Challenges of adapting the concepts

Overview of IoT Connectivity Methods and Technologies

  • Wireless 101
  • RF 101
  • ZigBee PRO, ZigBee 3.0 and ZigBee IP
  • 6LowPAN
  • RFID
  • Bluetooth and BLE (BT5.0)
  • Z-Wave
  • Home Automation (HA) Profile
  • Smart Energy (SE) Profile
  • Health Care
  • IEEE 802.15.4, IEEE 802.15.4e, 802.11ah
  • 802.11ah, Wi-Fi HaLow
  • Relay Access Point (AP)
  • Grouping of stations
  • Target Wake Time (TWT)
  • Speed Frame Exchange
  • Sectorization
  • GSM, CDMA, GPRS,3G, LTE, small cells, SATCOM
  • LTE and 5G
  • Sensors and sensor networks
  • Serial communication
  • Power consumption and optimization
  • MIPI, M-PHY, UniPro, SPMI, BIF,  SuperSpeed USB Inter-Chip (SSIC), Mobile PCIe (M-PCIe) and SPI
  • Wired connectivity
  • IPv4/IPv6
  • Ethernet/GigE
  • Real-time systems and embedded software
  • Big data
  • Analytics
  • Cloud computing and storage
  • Augmented Reality

Evaluation of IoT

  • Platforms
  • Mobile integration
  • Deployment
  • Data visualization
  • Convergence with social networks
  • Value chain and business models
  • User centric cloud based services
  • Analytical hierarchy process for technology selection
  • End-to-end security
  • Integration with IT systems
  • Cost/benefit constraints
  • End-to-end compatibility
  • Application architecture
  • Lifecycle solution management
  • Real-time response and delay
  • IoT and blockchain
  • IoT modeling and simulation
  • Programming the Internet of Things (IOT)
  • IoT wireless & cloud computing
  • Industrial IoT on cloud platforms
  • Architecting smart IoT devices
  • Internet of Things and embedded systems
  • IoT and embedded hardware
  • IoT python programming for the raspberry pi platform
  • Emerging IoT and big data technologies