- This course will provide you with skills necessary to apply machine learning techniques to data, and interpret and communicate their results.
- You will also begin to develop intuitions about when machine learning is an appropriate tool versus other statistical methods.
- This course will cover both supervised methods (e.g., k-nearest neighbors, naïve Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis and k-means clustering).
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- The supervised methods will focus primarily on “classic” machine learning techniques where features are designed rather than learned, although we will briefly look at recent deep learning models with neural networks.
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- This is an applied machine learning class that emphasizes the intuitions and know-how needed to get learning algorithms to work in practice, rather than mathematical derivations.
- The course will be taught in Python, primarily using the scikit-learn library.
- This course is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.
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