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Practical Machine Learning

Created/Published/Taught by:
Johns Hopkins University
Jeff Leek
Roger D. Peng
Brian Caffo

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Free? Partially: Some Free Content, Some Paid

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This is course 8 of 10 in the Coursera Data Science Specialization.

From Coursera:

“One of the most common tasks performed by data scientist 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 o building prediction functions including data collection, feature creation, algorithms, and evaluation


  • Week 1: Prediction, Errors, and Cross Validation
  • Week 2: The Caret Package
  • Week 3: Predicting with trees, Random Forests, & Model Based Predictions
  • Week 4: Regularized Regression and Combining Predictors

Recommended Prerequisites: Some programming experience

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