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0
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Hello, and welcome to Machine Learning with Python.
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In this course, you’ll learn how Machine Learning is used in many key fields and industries.
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For example, in the health care industry, data scientists use Machine Learning to predict
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whether a human cell that is believed to be at risk of developing cancer, is either benign
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or malignant.
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As such, Machine learning can play a key role in determining a person’s health and welfare.
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You’ll also learn about the value of decision trees and how building a good decision tree
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from historical data helps doctors to prescribe the proper medicine for each of their patients.
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You’ll learn how bankers use machine learning to make decisions on whether to approve loan
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applications.
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And you will learn how to use machine learning to do bank customer segmentation, where it
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is not usually easy to run for huge volumes of varied data.
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In this course, you’ll see how machine learning helps websites such as YouTube, Amazon, or
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Netflix develop recommendations to their customers about various products or services, such as
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which movies they might be interested in going to see or which books to buy.
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There is so much that you can do with Machine Learning!
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Here, you’ll learn how to use popular python libraries to build your model.
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For example, given an automobile dataset, we use the sci-kit learn (sklearn) library
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to estimate the Co2 emission of cars using their Engine size or Cylinders.
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We can even predict what the Co2 emissions will be for a car that hasn’t even been
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produced yet!
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And we’ll see how the telecommunications industry can predict customer churn.
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You can run and practice the code of all these samples using the built-in lab environment
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in this course.
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You don’t have to install anything to your computer or do anything on the cloud.
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All you have to do is click a button to start the lab environment in your browser.
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The code for the samples is already written using python language, in Jupyter notebooks,
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and you can run it to see the results, or change it to understand the algorithms better.
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So, what will you be able to achieve by taking this course?
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Well, by putting in just a few hours a week over the next few weeks, you’ll get new
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skills to add to your resume, such as regression, classification, clustering, sci-kit learn
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and SciPy.
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You’ll also get new projects that you can add to your portfolio, including cancer detection,
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predicting economic trends, predicting customer churn, recommendation engines, and many more.
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You’ll also get a certificate in machine learning to prove your competency, and share
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it anywhere you like online or offline, such as LinkedIn profiles and social media.
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So let’s get started.