Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. In this course, youll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, youll learn all about neural networks through an example of DNA classification data. Youll explore networks, implement them, and classify them. After that, youll see how to tune hyper-parameters using a data set of sonar data and youll get to know their properties. Next, youll understand unsupervised learning with an example of clustering politicians, where youll explore new patterns, understand unsupervised learning, and visualize and cluster the data. Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, well offer some suggestions for those who wish to practice the concepts further.
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