Industrial Training on Artificial Intelligence (AI) & Internet of Things (IoT) for College Faculties

The global artificial intelligence market is valued at USD 39.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027.IoT is the technology of the day which touches and transforms the every aspect of our real life.

  • Duration : 8 Week Ends
  • Training Starts On : 26-June-2021
  • English


Industrial Training on  Artificial Intelligence (AI)  & Internet of Things (IoT)  for College Faculties
  • Online Bootcamp

    Learners

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  • Online Bootcamp

    Companies

    628+

  • Online Bootcamp

    No. of Openings

    2187+

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    Ranking

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Course Overview

We have specially designed a customized Industrial Training curriculum on Artificial Intelligence (AI) & Internet of Things (IoT) for college faculties to meet industry requirements. This course aims to get the future technology knowledge and to acquire hands-on exposure in Artificial Intelligence and Internet of Things. The faculties will experiment all kinds of problems specific to logical solutions with AI & IoT Technology.

The global artificial intelligence market is valued at USD 39.9 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 42.2% from 2020 to 2027. The continuous research and innovation directed by the tech giants are driving the adoption of advanced technologies in industry verticals, such as automotive, healthcare, retail, finance, and manufacturing. However, technology has always been an essential element for these industries, but AI has brought technology at the centre of the organizations.

IoT is the technology of the day which touches and transforms the every aspect of our real life. IoT has given a concept of Machine to Machine (M2M) communication. Companies like Microsoft and SAP have implemented strategies to capitalize on Internet of Things so that you can just set up your business and start making it thrives. This technical training in AI with IoT makes the college faculties to keep them updated to future technologies.

Key Features

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Course Content

  • Lecture: 01
    Environment Setup
  • Lecture: 02
    Variable Types and Basic Operators
  • Lecture: 03
    Decision Making and Loops
  • Lecture: 04
    Strings
  • Lecture: 05
    Lists
  • Lecture: 06
    Tuples
  • Lecture: 07
    Dictionary
  • Lecture: 08
    Functions
  • Lecture: 09
    Classes and Objects
  • Lecture: 01
    Introduction and Environment
  • Lecture: 02
    Ndarray Object
  • Lecture: 03
    Data Types
  • Lecture: 04
    Array Attributes and Creation Routines
  • Lecture: 05
    Indexing & Slicing
  • Lecture: 06
    Advanced Indexing
  • Lecture: 07
    Iterating Over Array
  • Lecture: 08
    Histogram Using Matplotlib
  • Lecture: 01
    Introduction and Installation
  • Lecture: 02
    Pyplot API
  • Lecture: 03
    Simple Plot
  • Lecture: 04
    PyLab module
  • Lecture: 05
    Axes Class
  • Lecture: 06
    Subplots() Function
  • Lecture: 07
    Bar Plot
  • Lecture: 08
    Histogram
  • Lecture: 01
    Introduction and Environment Setup
  • Lecture: 02
    Introduction to Data Structures
  • Lecture: 03
    Series
  • Lecture: 04
    DataFrame
  • Lecture: 05
    Panel
  • Lecture: 06
    Basic Functionality
  • Lecture: 07
    Categorical Data and Visualization
  • Lecture: 08
    Read/Write-Excel, Text, CSV
  • Lecture: 01
    Introduction and Installation
  • Lecture: 02
    Steps to implement
  • Lecture: 03
    Introduction & Installation
  • Lecture: 04
    Steps to implement
  • Lecture: 05
    Introduction and Installation
  • Lecture: 06
    Steps to implement
  • Lecture: 01
    Mean, Median, Mode, Variance
  • Lecture: 02
    Correlation, Regression
  • Lecture: 03
    Hypothesis testing, Kurtosis
  • Lecture: 04
    Skewness
  • Lecture: 05
    Percentiles and Outliers
  • Lecture: 06
    Normal Distribution
  • Lecture: 07
    Vectors
  • Lecture: 08
    Scalar, matrices
  • Lecture: 09
    Tensor
  • Lecture: 10
    Eigenvalues and eigenvectors
  • Lecture: 01
    Training Data
  • Lecture: 02
    Test Data
  • Lecture: 03
    Fitting Data
  • Lecture: 04
    Loss function
  • Lecture: 05
    Optimization
  • Lecture: 06
    Metrics
  • Lecture: 01
    Regression (Supervised Learning)
  • Lecture: 02
    Linear Regression (Supervised Learning)
  • Lecture: 03
    Polynomial Regression (Supervised Learning)
  • Lecture: 04
    Classification (Supervised Learning)
  • Lecture: 05
    Logistic Regression (Supervised Learning)
  • Lecture: 06
    SVM, KNN (Supervised Learning)
  • Lecture: 07
    Decision Tree, Random Forest (Supervised Learning)
  • Lecture: 08
    Clustering (Unsupervised Learning)
  • Lecture: 09
    K-Means clustering (Unsupervised Learning)
  • Lecture: 10
    Dimensionality reduction (Unsupervised Learning)
  • Lecture: 11
    Principle Component Analysis (PCA) (Unsupervised Learning)
  • Lecture: 12
    Yarowsky algorithm (Semi supervisor Learning)
  • Lecture: 13
    Self-training classifier(Semi supervisor Learning)
  • Lecture: 14
    Reinforcement learning Framework (Reinforcement learning)
  • Lecture: 15
    Types of RL Systems (Reinforcement learning)
  • Lecture: 16
    Q-learning (Reinforcement learning)
  • Lecture: 01
    Fundamental of Neural Networks
  • Lecture: 02
    Single –Layer Perceptron (SLP)
  • Lecture: 03
    Multi –Layer Perceptron (MLP)
  • Lecture: 04
    Feed Forward Networks (FFN)
  • Lecture: 05
    Convolutional Neural Networks (CNN)
  • Lecture: 06
    Back-Propagation Networks (BPN)
  • Lecture: 07
    Recurrent Neural Networks (RNN)
  • Lecture: 01
    What is the IoT and why is it important?
  • Lecture: 02
    Evolution of IoT
  • Lecture: 03
    Components of IoT System
  • Lecture: 04
    IoT applications
  • Lecture: 05
    Sensors (Input Device) Principle and Working with examples
  • Lecture: 06
    Actuators (Output Device) Principle and Working with examples
  • Lecture: 07
    Controller/IoT Development Kit
  • Lecture: 08
    Arduino controller
  • Lecture: 09
    Node MCU-ESP8266
  • Lecture: 10
    Raspberry pi
  • Lecture: 11
    Cloud Storage
  • Lecture: 01
    IoT proposed layers Architecture
  • Lecture: 02
    IoT Cloud Infrastructure
  • Lecture: 03
    Cloud computing
  • Lecture: 04
    Cloud types, Cloud Services
  • Lecture: 05
    Data Security
  • Lecture: 01
    Arduino -Hardware Description
  • Lecture: 02
    IDE Installation
  • Lecture: 03
    Micro-controller programming using Arduino and ESP8266
  • Lecture: 04
    Tinkercad tool - Arduino programming
  • Lecture: 05
    Street Light Automation
  • Lecture: 01
    Intelligent Home Automation using IoT (Blynk App)
  • Lecture: 02
    Building IoT Weather Station using Ubidots Cloud
  • Lecture: 03
    Smart Irrigation System
  • Lecture: 01
    Face Detection Security System using Raspberry Pi
  • Lecture: 02
    AWS Instance Creation
  • Lecture: 01
    Industry 4.0

Course Orientation Video

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Industry Project

  • Street Light Automation
  • Intelligent Home Automation using IoT (Blynk App)
  • Building IoT Weather Station using Ubidots Cloud
  • Smart Irrigation System

Batch Details

Training Duration: 8 Week Ends

Time : 10.00 AM to 1.00 PM

Starts on : 26-June-2021

Who can Attend

  • Professors
  • Head of the Departments - HoDs
  • Principals
  • Associate/Assistant Professor
  • Lectures/Ph.D Scholars
  • CAS – Career Advancement Scheme Industrial Training Aspirants
  • Professionals or Job Seekers who want to up-skill in AI & IoT

Pre-requisites

  • PC/ Laptop with Internet Connection
  • Basic Knowledge in any Programming Language

Training Outcome

  • You can be in trend with the latest technology and its tools.
  • You can apply the basic principles, models, and algorithms of AI & IoT to recognize, model, and solve problems in the analysis and design of information systems.
  • You can analyse the structures and algorithms of a selection of techniques related to searching, reasoning, machine learning, and language processing.
  • You can review research articles from well-known journals and conference proceedings regarding the theories and applications of AI & IoT.
  • You can carry out a research project and write a research proposal, report and paper.

Skills Covered

  • Python
  • Essential Statistics
  • Essential Mathematics
  • Data Processing
  • Machine Learning
  • IoT Applications
  • IoT  Components
  • IoT Architecture
  • Arduino UNO
  • Node MCU
  • Raspberry pi

Learning path

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Tools Used

Skilltechnika's Advantage

Live Interaction

Live Interaction Session

Hands-On Practice

Hands On Practice

Real Time Project

Real Time Project

Curriculum

Latest Curriculum

Recorded Session

Recorded Session Videos

Industry Connects

Industry Connects

Peer Learning

Peer Learning

Mentor Support

Mentor Support

Assessments

Assessments

Training Options

Live Online Session
$ 450
GROUP ENROLLMENT
Customized to your team's needs

CourseCertificate

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