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Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

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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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