Applied machine learning: algorithms course. Given the growing importance of machine learning in almost every field, professionals need a deeper understanding and practical approach to effectively implement machine learning algorithms. This course covers common machine learning algorithms. Course instructor Matt Harrison focuses on shallow learning algorithms and covers PCA, clustering, linear and logistic regression, decision trees, random forests, and slope boosting. By taking this course with Matt, you’ll understand common machine learning algorithms, learn their pros and cons, and develop practical skills in using them by following problems and solutions on GitHub Codespaces.
What will you learn
- Learn common machine learning algorithms such as K-means, PCA, linear and logistic regression, decision trees, random forests, and slope boosting.
- Find out the pros and cons of each algorithm.
- Develop your practical skills in using machine learning algorithms to solve real-world problems.
- Learn how to use GitHub codespaces to hone your skills.
This course is suitable for people who
- Interested in learning how to use machine learning algorithms to solve real-world problems.
- Have prior knowledge of machine learning and strive for a deeper and more practical understanding of common algorithms.
- Want to improve your machine learning skills with GitHub Codespaces.