AI News, scikit-learn video #1: Intro to machine learning with scikit-learn

scikit-learn video #1: Intro to machine learning with scikit-learn

As a data science instructor and the founder of Data School, I spend a lot of my time figuring out how to distill complex topics like 'machine learning' into small, hands-on lessons that aspiring data scientists can use to advance their data science skills.

As a practitioner of machine learning, there's a lot to like about scikit-learn: It provides a robust set of machine learning models with a consistent interface, all of the functionality is thoughtfully designed and organized, and the documentation is thorough and well-written.

However, I personally believe that getting started with machine learning in scikit-learn is more difficult than in a language such R, as I explain here: In R, getting started with your first model is easy: read your data into a data frame, use a built-in model (such as linear regression) along with R's easy-to-read formula language, and then review the model's summary output.

My primary goal with this video series, 'Introduction to machine learning with scikit-learn', is to help motivated individuals to gain a thorough grasp of both machine learning fundamentals and the scikit-learn workflow.

(The series does presume basic familiarity with Python, though next week I'll suggest some resources for learning Python if you're new to the language.) For those who successfully master the basics (or are already intermediate-level scikit-learn users), my secondary goal is to dive into more advanced functionality later in the series.

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