From Beginner To Intermediate

Data Science with R: Machine Learning

A class for computer-literate people with some programming background who wish to learn more about R programming.

Data Science with R-Machine Learning
Certificate Awarded
Best Data Science Bootcamp Switchup
4.89 / 5
(317 Reviews)
Best Data Science Bootcamp
5 Years Running
Course Report Rating
4.84 / 5
( 322 Reviews)
Best Data Science Bootcamp
5 Years Running

Data Science with R: Machine Learning

This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and Naïve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.

Unit 1: Foundations of Statistics and Simple Linear Regression
  • Understand your data
  • Statistical inference
  • Introduction to machine learning
  • Simple linear regression
  • Diagnostics and transformations
  • The coefficient of determination
Unit 2: Multiple Linear Regression and Generalized Linear Model
  • Multiple linear regression
  • Assumptions and diagnostics
  • Extending model flexibility
  • Generalized linear models
  • Logistic regression
  • Maximum likelihood estimation
  • Model interpretation
  • Assessing model fit
Unit 3: kNN and Naive Bayes, the Curse of Dimensionality
  • The K-Nearest Neighbors Algorithm
  • The choice of K and distance measure
  • Conditional probability: Bayes’ Theorem
  • The Naive Bayes’ Algorithm
  • The Laplace estimator
  • Dimension reduction
  • The PCA procedure
  • Ridge and Lasso regression
  • Cross-validation
Unit 4: Tree Models and SVMs
  • Decision trees
  • Bagging
  • Random forests
  • Boosting
  • Variable Importance
  • Hyperplanes and maximal margin classifier
  • Sort margin and support vector classifier
  • Kernels and support vector machines
Unit 5: Cluster Analysis and Neural Networks
  • Cluster analysis
  • K-means clustering
  • Hierarchical clustering
  • Neural networks and perceptrons
  • Sigmoid neurons
  • Network topology and hidden features
  • Back propagation learning with gradient descent

*We do not offer this course at this moment. Please join our waiting list to be notified when it becomes available again.

Customer Reviews

Took the weekend course for Machine Learning with R. Course was very helpful in helping me understand the basics of Machine Learning, different models. My instructor was Luke. He was very helpful and would spend enough time covering each topic. He even took an additional class because he didn't want to rush through the material. Overall I am quite satisfied with the results. Would recommend Luke to anyone else who is interested to venture into Machine Learning field.

Rahul Bhat

NYC Data Science Academy (NYC Data Science Academy) provided the platform to pursue my dream career.  The curriculum is well thought out, with detailed notes, hands-on projects, and great hiring partners. NYC Data Science Academy gave me the tools to be come a data scientist, and the exposure to land the job.  Truly one of the best investments I have ever made!

Kelly Mejia Breton, Associate Director, Marketing ScienceMindshare
View more customer reviews

Reasons to Enroll


Our instructors are consistently highly rated by their students.  They not only know their subject cold, they are experts at teaching you.


Our curriculum is continuously updated to reflect the latest technology trends.


Learn on the latest technology.  When you complete this course, you will have a solid foundation in python and the use of the tools.

Kathy Liu

Kathy holds a PhD in Mathematics from New York University and a master degree from Georgetown University. She is specialized in information theory and probability. Kathy is passionate about teaching and her mathematics and statistics classes at NYU are so popular that seats are filled in very quickly. After serving as a Data Science Consultant in a reinsurance company, Kathy realizes the power of data analytics and the fun of story-telling, then she starts to use statistical models and data visualization tools to conduct collaborative research in Stern School of Business and Courant Institute of Mathematical Sciences at NYU. When not working, Kathy can be found watching Broadway shows in theater district, practicing golf at Chelsea Piers and hiking in upstate New York.

Kathy Liu - Data Science Instructor

Your Certificate of Completion


  • Knowledge of R programming
  • Able to munge, analyze, and visualize data in R


Certificates are awarded at the end of the program at the satisfactory completion of the course. Students are evaluated on a pass/fail basis for their performance on the required homework and final project (where applicable). Students who complete 80% of the homework and attend a minimum of 85% of all classes are eligible for the certificate of completion.