The following resources are a starting point for machine learning and R programming skills:
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R for Data Science: Visualize, Model, Transform, Tidy, and Import Data by Hadley Wickham and Garrett Grolemund
This free online book is a great starting point for R, and specifically focuses on learning how to use R for data science purposes. It is particularly useful for teaching skills related to ggplot.
This is a free Coursera course that gives an overview on appropriate times to use linear regression, checking model assumptions, interpreting outputs, and more. See an introductory video by clicking HERE.
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An Introduction to Statistical Learning, with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
This book provides an overview of the field of statistical learning, and includes topics on linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented.
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Statistical Learning: a Stanford School of Humanities online course
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. This is not meant to be a math-heavy class. Instead, Stanford focuses on describing methods without heavy reliance on formulas and complex mathematics. These lectures cover all the material in the book listed above, An Introduction to Statistical Learning, with Applications in R, and the pdf will be available for free for course participants.
This YouTube channel presented by Josh Starmer is a helpful resource for visual learners on statistics and machine learning.