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Berkeley Global
Once a niche set of tools for statisticians, programmers and quants, machine learning has spread in popularity to a wide variety of applications and disciplines. Get a practical, hands-on introduction to machine learning using R, an open-source, statistical programming language. Real-world examples teach you how to solve problems using machine learning in your own careers and fields.
Taught from a data science practitioner’s perspective, this course provides an easy-to-follow roadmap and practical tools for model training, testing, refinement and deployment. You explore systematic methods for problem framing and pragmatic steps that can be taken in identifying solutions. You have the opportunity to apply these methods and techniques to coding assignments, peer discussions and live webinars with practitioners in the field.
Prerequisites:
Basic familiarity with statistical terminology as covered in a first-semester undergraduate course (Introduction to Statistics STAT X10, for example). The topics of such a course might include, but are not limited to the following:
- Probability and descriptive statistics
- Linear regression
- Hypothesis testing
- Interval estimation
Beginner-level understanding of the R programming language, but experience is not necessary. Beginners with pre-existing capabilities might be able to do the following:
- Use basic R functions
- Install R libraries
- Explore data or run a regression on a dataset in R
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