Machine Learning using R

Machine learning is becoming increasingly important, not only in data science, but also in bioinformatics. Machine learning gives you the opportunity to find hidden patterns in your data and draw predictive conclusions. In this workshop, you’ll learn key machine learning concepts and approaches, and get hands-on practice with simple examples.

You’ll be introduced to topics including:

• Supervised and unsupervised machine learning
• Training data and test data sets
• Bias and variance
• Cross validation
• Performance evaluation

The hands-on practice will focus on the algorithms:

• K-Nearest Neighbors (KNN)
• K-means clustering
• Random forest

Prior experience with R is required, please review the Introduction to R for data analysis workshop.

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