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DataCamp Supervised Learning with scikit-learn

Created/Published/Taught by:
DataCamp
Andreas Müller
Hugo Bowne-Anderson
Yashas Roy

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DataCamp

Free? Partially: Some Free Content, Some Paid

Cost Range:
$0.00 - $29.00

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From DataCamp:

“At the end of the day, the value of Data Scientists rests on their ability to describe the world and to make predictions. Machine Learning is the field of teaching machines and computers to learn from existing data to make predictions on new data – will a given tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam or not? In this course, you’ll learn how to use Python to perform supervised learning, an essential component of Machine Learning. You’ll learn how to build predictive models, how to tune their parameters and how to tell how well they will perform on unseen data, all the while using real world datasets. You’ll do so using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.”

This course consists of four chapters:

  1. Classification
  2. Regression
  3. Fine-tuning your model
  4. Preprocessing and pipelines

Recommended Prerequisites: Intro to Python for Data Science, Intermediate Python for Data Science, Statistical Thinking in Python (Part 1)

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