Introduction to R (edX 1.1)

Introduction to R (edX 1.2)

Lab exercises on Create and Name Vectors.

Introduction to R (edX 2.1)

Introduction to R (edX 2.2)

Introduction to R (edX 2.3)

Lab exercises on Create and Name Matrices.

Introduction to R (edX 3.1)

Introduction to R (edX 3.2)

Introduction to R (edX 3.3)

Introduction to R (edX 4.1)

Introduction to R (edX 5.1)

Lab exercises on Subset and Extend Lists.

Introduction to R (edX 5.2)

Introduction to R (edX 6.1)

Machine learning is the field that teaches machines and computers to learn from existing data to make predictions on new data: Will a tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam? In this course, you'll learn how to use Python to perform supervised learning, an essential component of machine learning. You'll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data—all while using real world datasets. You'll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

Learn how to build and tune predictive models and evaluate how well they'll perform on unseen data.

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