Why is it important to understand machine learning? Many machine learning techniques can solve interesting problems, from identifying email spam to classifying images. It is important to understand how libraries like scikit-learn work under the hood, to know their strengths and weaknesses, and to determine if they should be used for a given problem.
What you'll learn—and how you can apply it
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By the end of this course, you’ll understand:
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What machine learning is and the various algorithms under its umbrella
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How regression and classification techniques work, from linear regression to neural networks
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The API patterns of scikit-learn
And you'll be able to:
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Use scikit-learn to make predictions on different types of problems, from email spam to image recognition Pair the proper application of supervised machine learning algorithms to a given problem and context appropriately Explain how different machine learning algorithms work, including decision trees and neural networks This training is for you because...
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You’re a data science professional wanting to learn about ML and how it works. You work with data science teams and want to understand their ML capabilities. You want to become a machine learning or data engineer and want to take the first step in that career path Prerequisites
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Basic proficiency with Python (variables, loops, installing and importing packages, collections, list comprehensions, declaring NumPy arrays).