From the course: Applied Machine Learning: Foundations
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Tools for ML
From the course: Applied Machine Learning: Foundations
Tools for ML
Machine learning has progressed quickly in recent years, and the tools and techniques continue to evolve. To be effective with machine learning, you'll need to use a programming language. Python is the most popular programming language and a great place to start. Others will use R and those looking to squeeze the last bit of performance will use a lower-level language like C or Rust. However, if you're smart about how you use Python, you can get great performance without resorting to these lower-level languages. Scikit-Learn is a popular library for working with tabular data. It has many algorithms for creating both supervised and unsupervised models. It also provides pipelines for creating workflows. Another advantage of Scikit-Learn is that it has a consistent API. Once you know how to create a regression model using linear regression, it's trivial to try a different model such as the decision tree, and more advanced libraries like XGBoost and CatBoost have followed the lead of…
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