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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: 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) print(sample

Quick Guide: Machine Learning Examples and Uses

Machine learning

I want to share with you the best real-time examples on machine learning. Because of new computing technologies, machine learning today is not like machine learning of the past. 

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Machine learning use cases
  • The heavily hyped, self-driving Google car? The essence of machine learning. 
  • Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life. 
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. 
  • Fraud detection? One of the more obvious, important uses in our world today.
Best example: "pattern recognition" is best example for Machine Learning
Where can you apply machine learning. The following are the key areas you can apply machine learning.
  1. Fraud detection.
  2. Web search results.
  3. Real-time ads on web pages and mobile devices.
  4. Text-based sentiment analysis.
  5. Credit scoring and next-best offers.
  6. Prediction of equipment failures.
  7. New pricing models.
  8. Network intrusion detection.
  9. Pattern and image recognition.
  10. Email spam filtering.

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