Practical Machine Learning
Learn the basics of prediction and machine learning in this practical course. Covering concepts like overfitting and error rates, you’ll explore regression, classification trees, Naive Bayes, and random forests. Discover the complete process of building prediction functions, from data collection to evaluation.
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
