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How to Work With Tuple in Python

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Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: Tuple example my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8

How to Use ML in IoT Projects

Why you need machine learning skills? Let us start with Big data. Big data relates to extremely large and complex data. So, the availability of huge data makes machine learning is popular to use in future prediction.

6 ideas how to use ML in IoT

  1. Machine Learning comprises algorithms that learn from data, make predictions based on their learning, and have the ability to improve their outcomes with experience. Due to the enormity of data involved with Machine Learning, various technologies and frameworks have been developed to address the same. Hadoop is an open-source framework targeted for commodity hardware to address big data scale.
  2. The distributed design of the Hadoop framework makes it an excellent fit to crunch data and draw insights from it by unleashing Machine Learning algorithms on it. 
  3. So, the true value of IoT comes from ubiquitous sensors’ relaying of data in real-time, getting that data over to Hadoop clusters in a central processing unit, absorbing the same, and performing Machine Learning on data to draw insights; all at petabyte scale or more.
  4. In reviewing the use cases and challenges from preceding sections, one thing is very clear. That is to do with the quickness with which certain analytics must be performed. Imagine sending a critical alert late because computing could not be done any faster. Two key gaps here include absorbing incoming data at such a high rate reliably and in observing that Hadoop was not created for real-time streaming data.
  5. It was originally envisaged as a framework for batch processing. Innovators have responded to those challenges well. Let us review some of those technologies now.
  6. SAP HANA with the internet of things came into the picture with real-time processing of data compared to Hadoop which is only batch processing. 
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