Practical Machine Learning is a free online course, which is a part of data science specialisation. Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialisation.
About the Course
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.
Upon completion of this course students will understand the components of a machine learning algorithm. They will also know how to apply multiple basic machine learning tools and also learn to apply these tools to build and evaluate predictors on real data.
October 6, 2014 - November 3, 2014
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