Seminar on Machine Learning with IoT Explained

 
A SEMINAR ON-
MACHINE LEARNING WITH  IOT
 
GUIDED BY:
PROF . B.A.KHIVSARA
 
PREPARED BY:
PRAJAKTA  R. AHER
 
MACHINE LEARNING WITH IOT
 
 
OUTLINES
 
Introduction
Literature survey
Working
Advantages and disadvantages
Conclusion
References
 
INTRODUCTION   OF MACHINE LEARNING
 
Machine Learning is the scientific study of algorithm and statistical that computer system use to
effectively perform a specific task without using explicit instructions  on models and inference instead.
 
Machine learning algorithms build a mathematical model of sample data, known as “training data”, in
order to make predictions or decisions without being explicitly programmed to perform the task .
Machine learning has experienced a boost in popularity among industrial companies thanks to the hype
surrounding the IOT.
 
 
 
 
 
  Internet of things is a computing concepts that describes the idea of everyday physical
objects being connected to the internet and being able to identify themselves to other
device.
 Internet of things provides the great features with Machine Learning . It can change the
world with better features.
 
 
INTERNET OF THINGS
 
MACHINE  LEARNING WITH IOT
 
LITERATURE SURVEY OF MACHINE LEARNING
 
History
 and relationships to other fields. Arthur Samuel, an American pioneer in the field of
computer gaming and artificial 
intelligence
, coined the term "
Machine Learning
" in 1959
while at IBM. As a scientific endeavour, 
machine learning
 grew out of the quest for artificial
intelligence
.
The ML  programming language was developed in the 1970 by Robin Milner and  his
colleagues at the University of Edinburgh during their work on the logic  for computational
Functions.
 
 
MACHINE LEARNING ALGORITHMS
 
Supervised learning
       
Prediction
Classification (discrete labels), Regression (real values)
Unsupervised learning
Clustering
Probability distribution estimation
Finding association (in features)
Dimension reduction
Reinforcement learning
Decision making (robot, chess machine)
 
ALGORITHMS  OF MACHINE LEARNING
 
ALGORITHMS
Supervised learning
Unsupervised learning
Semi-supervised learning
 
APPLICATIONS OF MACHINE LEARNING WITH
IOT
 
Smart Home.
Weather forecast.
Face detection
Object detection and recognition
Prediction systems
Multimedia event detection
Value saving in Industrial application
 
 
Smart Home is building Automation  for  the home.
A  Smart Home system should be able to predict a user’s behaviour  based on historical data and
develop the so-called situational awareness .
Machine learning algorithms trained on smart home sensor data can predict when an individual
faces difficulty while performing everyday activities.
Applications-
       1.Face Recognition
       2.Biometric Access Control (locks)
       3.Natural  language  Processing(Voice Recognition)
 
 
1.SMART  HOME USING MACHINE LEARNING
 
SMART HOME WITH MACHINE LEARNING
 
2.   WEATHER    FORECASTING
 
Machine Learning to do weather forecast (chance of rain) using the temperature and humidity data from your Azure IOT hub.
 The chance of rain is the output of a prepared weather prediction model. The model is built upon historic data to forecast
chance of rain based on temperature and humidity.
Run the client application to start collecting and sending temperature and humidity data to your IOT hub.
Sensors
      1.Temperature Sensor
      2.Humidity Sensor
      3.Pressure ensor
 
WORKING OF WEATHER FORECAST -
 
 
 
CONCLUSION
 
 We have a simple overview of some techniques and algorithms in machine learning.  In the
future, machine learning will play an important role in our daily life.
The future of Machine Learning with IOT  is virtually unlimited due to advances in
technology and consumers desire to integrate devices.
The design phase of IOT is very fast field with umpteen number of challenges and integrate
with machine learning provides very high features.
 
REFERENCES
 
[1] 
https://en.wikipedia.org/wiki/Machine_learning
[2] 
https://en.wikipedia.org/wiki/Internet_of_things
[3] 
https://www.sas.com/en_in/insights/articles/big-data/machine-learning-brings-
concrete-aspect-to-iot.html
[4] 
https://data-flair.training/blogs/iot-and-machine-learning/
[5] 
https://www.google.com/search?client=firefox-b-d&q=pictures
 
THANK    YOU!!!
 
             ANY QUESTIONS ???
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Explore the intersection of Machine Learning and Internet of Things (IoT) in this informative seminar. Discover the principles, advantages, and applications of Machine Learning algorithms in the context of IoT technology. Learn about the evolution of Machine Learning, the concept of Internet of Things, and the essential literature survey in the field. Delve into the diverse Machine Learning algorithms and their practical use cases like supervised learning, unsupervised learning, and reinforcement learning. Gain insights into how these technologies are shaping various industries and driving innovation.

  • Machine Learning
  • IoT
  • Seminar
  • Algorithms
  • Technology

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  1. A SEMINAR ON- MACHINE LEARNING WITH IOT PREPARED BY: PRAJAKTA R. AHER GUIDED BY: PROF . B.A.KHIVSARA

  2. MACHINE LEARNING WITH IOT

  3. OUTLINES Introduction Literature survey Working Advantages and disadvantages Conclusion References

  4. INTRODUCTION OF MACHINE LEARNING Machine Learning is the scientific study of algorithm and statistical that computer system use to effectively perform a specific task without using explicit instructions on models and inference instead. Machine learning algorithms build a mathematical model of sample data, known as training data , in order to make predictions or decisions without being explicitly programmed to perform the task . Machine learning has experienced a boost in popularity among industrial companies thanks to the hype surrounding the IOT.

  5. INTERNET OF THINGS Internet of things is a computing concepts that describes the idea of everyday physical objects being connected to the internet and being able to identify themselves to other device. Internet of things provides the great features with Machine Learning . It can change the world with better features.

  6. MACHINE LEARNING WITH IOT

  7. LITERATURE SURVEY OF MACHINE LEARNING History and relationships to other fields. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM. As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. The ML programming language was developed in the 1970 by Robin Milner and his colleagues at the University of Edinburgh during their work on the logic for computational Functions.

  8. MACHINE LEARNING ALGORITHMS

  9. ALGORITHMS OF MACHINE LEARNING Supervised learning Prediction Classification (discrete labels), Regression (real values) Unsupervised learning Clustering Probability distribution estimation Finding association (in features) Dimension reduction Reinforcement learning Decision making (robot, chess machine)

  10. ALGORITHMS Unsupervised learning Supervised learning Semi-supervised learning

  11. APPLICATIONS OF MACHINE LEARNING WITH IOT Smart Home. Weather forecast. Face detection Object detection and recognition Prediction systems Multimedia event detection Value saving in Industrial application

  12. 1.SMART HOME USING MACHINE LEARNING Smart Home is building Automation for the home. A Smart Home system should be able to predict a user s behaviour based on historical data and develop the so-called situational awareness . Machine learning algorithms trained on smart home sensor data can predict when an individual faces difficulty while performing everyday activities. Applications- 1.Face Recognition 2.Biometric Access Control (locks) 3.Natural language Processing(Voice Recognition)

  13. SMART HOME WITH MACHINE LEARNING

  14. 2. WEATHER FORECASTING Machine Learning to do weather forecast (chance of rain) using the temperature and humidity data from your Azure IOT hub. The chance of rain is the output of a prepared weather prediction model. The model is built upon historic data to forecast chance of rain based on temperature and humidity. Run the client application to start collecting and sending temperature and humidity data to your IOT hub. Sensors 1.Temperature Sensor 2.Humidity Sensor 3.Pressure ensor

  15. WORKING OF WEATHER FORECAST -

  16. CONCLUSION We have a simple overview of some techniques and algorithms in machine learning. In the future, machine learning will play an important role in our daily life. The future of Machine Learning with IOT is virtually unlimited due to advances in technology and consumers desire to integrate devices. The design phase of IOT is very fast field with umpteen number of challenges and integrate with machine learning provides very high features.

  17. REFERENCES [1] https://en.wikipedia.org/wiki/Machine_learning [2] https://en.wikipedia.org/wiki/Internet_of_things [3] https://www.sas.com/en_in/insights/articles/big-data/machine-learning-brings- concrete-aspect-to-iot.html [4] https://data-flair.training/blogs/iot-and-machine-learning/ [5] https://www.google.com/search?client=firefox-b-d&q=pictures

  18. THANK YOU!!!

  19. ANY QUESTIONS ???

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