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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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IBM these are analytics solutions offering to different industries

IBM analytics

Every industry has its own particular big data challenges. Banks need to analyze streaming transactions in real time to quickly identify potential fraud. Utility companies need to analyze energy usage data to gain control over demand. 

Retailers need to understand the social sentiment around their products and markets to develop more effective campaigns and promotions. Analytics solutions help organizations take control of big data and uncover the insights they need to make the best decisions.

IBM has Analytics Solutions in various lines:

  • Banks: Apply analytics to improve customer experiences and operational efficiency, and integrate risk into daily decision making.
  • Communication:Uncover insights about customers, network performance and market trends to make better business decisions.
  • Retail: Build lifetime customer relationships by meeting demands for innovative products while containing costs.
  • Education: Make more informed decisions to improve student performance and increase operational efficiency.
  • Energy Analytics: Transform your utility network and optimize customer operations with smarter energy systems.
  • Government: Gain insight into program performance, traffic patterns, public safety threats and more to better protect and serve citizens.
  • Healthcare: Anticipate, shape and optimize business and patient outcomes, and enable evidence-based, personalized medicine.
  • Industrial: Apply analytics in aerospace, defense, automotive, electronics, chemicals, petroleum, or industrial products companies.
  • Insurance: Deploy analytics at the point of impact to support better decisions about underwriting, claims and other areas of your business.
  • Life Sciences: Act on insights to drive growth, enhance relationships across the ecosystem and improve clinical development processes.
  • Media: Use analytics to provide a differentiated customer experience and drive operational transformation.
  • Transportation: Enhance services, manage capacity, and maximize the availability of assets and infrastructure.

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