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Build 25 Real World Data Science & Machine Learning Projects


Build 25 Real World Data Science & Machine Learning Projects - 
Practical Data Science Course: Learn To Build Machine Learning, Data Science Projects & Case Studies With Python

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What you'll learn
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Clean your input data to remove outliers
  • Learn to pre process data, clean data, and analyze large data.

Machine learning has inserted itself into the fiber of our everyday lives – even without us noticing. Machine learning algorithms have been powering the world around us, and this includes product recommendations at Walmart, fraud detection at various top-notch financial institutions, surge pricing at Uber, as well as content used by LinkedIn, Facebook, Instagram, and Twitter on users’ feeds, and these are just a few examples, grounded directly in the daily lives we live.

This being said, it goes without saying that the future is already here – and machine learning plays a significant role in the way our contemporary imagination visualises it. Mark Cuban, for instance, has said: “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise you’re going to be a dinosaur within 3 years.”

Machine learning makes a mockery of anything that can be called “important” – both at a financial as well as a global scale. If you are looking to take your career to another level, Machine Learning can do that for you. If you are looking to involve yourself in something that will make you part of something that is global as well as contemporary relevance, Machine Learning can do that for you as well.

Machine learning covers significant ground in various verticals – including image recognition, medicine, cyber security, facial recognition, and more. As an increasing amount of businesses are realising that business intelligence is profoundly impacted by machine learning, and thus are choosing to invest in it.

Who this course is for:
  • Beginners In Data Science

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