In this course, you’ll learn how to build a supervised machine learning model in Python, as well as how to train and improve it for better performance and accuracy.
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4.8 (359 reviews)
“I originally tried DataCamp and a machine learning course on Coursera. But I wanted something more practical, that worked with real data sets using Python and R. Dataquest is a great value, given the interesting data sets. It made sense — invest in your education and get a higher-paying job.”
In this course, you’ll learn how to develop a machine learning workflow for classification tasks using scikit-learn. You’ll learn how to build and implement the k-nearest neighbors algorithm using pandas and scikit-learn. Finally, you’ll learn to train, validate, and improve your machine learning model for better performance and accuracy using techniques like tuning hyperparameters.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills to complete a project to predict heart disease.
Establishing a machine learning workflowImplementing the k-nearest neighbors algorithm for a classification task using pandasImplementing the k-nearest neighbors algorithm using scikit-learnImproving model performance using hyperparameters Loading lessons...
Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our course, you'll master data skills and grow your career.
We believe so strongly in our courses that we offer a full satisfaction guarantee. If you complete a career course on Dataquest and aren't satisfied with your outcome, we'll give you a refund.
Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.
Build confidence with our in-depth projects, and show off your data skills.
Work with real data from day one with interactive lessons and hands-on exercises.
Impress employers by completing a capstone project and certifying it with an expert review.
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