Wrap Up

That’s the end of our course! We’ve covered a range of topics related to machine learning (ML) with scikit-learn.

Summary

Our course began with an introduction to ML, laying the foundation for our exploration of the world of data-driven predictions before delving into loading and preprocessing data. We’ve also explored both supervised and unsupervised learning algorithms, learning how to evaluate them. Finally, we examined some powerful tools, such as pipelines and feature importance, that can take our ML projects to the next level.

Overall, this course provided a solid foundation in ML with scikit-learn, exploring different coding challenges of increasing complexity while ensuring a hands-on approach.

Next steps

To further advance this learning journey, try practicing the techniques we’ve seen in this course with other datasets and continue to explore a wider array of ML methods and techniques. ML algorithms and libraries change all the time, so it’s important to keep up-to-date with new techniques.