Advanced

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.

Class Date

Clear

* Tuition paid for part-time courses can be applied to the Data Science Bootcamps if admitted within 9 months.

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

Find out more information about our professional development courses.

DOWNLOAD COURSE INFORMATION

Description

Product Description

Course Overview

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.

Prerequisites

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

Certificate

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.

Bundle Up, Learn More and Save More!

Browse Bundles

Bundle Up, Learn More and Save More!

Browse Bundles

Syllabus

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

Our Alumni Feedback

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

I studied mechanical engineering and physics for my undergrad at a top university and work in product management with a focus on search. I took this class to satisfy a personal interest in the subject matter and familiarize myself enough with the fundamentals of machine learning to be able to explore the field more deeply on my own. I was also motivated by a career interest: the subject matter is highly relevant to my domain, and I feel that developing an understanding of the concepts and how to deploy them myself will make me better at my job long-term. Prior to enrolling in the class, I spent roughly 8-10 hours learning R and felt sufficiently prepared (I had some previous programming experience). In the end I was extremely happy with this class (Machine Learning in R on Saturdays, 8 hrs at a time). The curriculum and content were excellent, the instructor, Luke, was fantastic and the assignments were challenging and informative. I felt the course did a really great job of driving home the core fundamentals of each subject with a focus on statistics, mathematical theory, derivations and best practices. We covered a LOT of material, yet the material had a lot of depth. I thought the sequencing of the subject matter was very well thought out as well. The class was demanding and had the caliber of a graduate-level course. The course also struck a very nice balance between theory and implementation. After learning about a new model, we would immediately implement it in class using R on our own machines. Luke did a particularly great job at relating the implementation back to the concepts and teaching us how to interpret outcomes of our analyses (I can’t stress enough how important this latter point was for me). He has a really strong grasp of the subject matter, he’s very patient and responsive to questions, offers a lot of insightful commentary on the theory, implementations, and best practices, and he cares about his students a lot. The homework assignments complement the class nicely as well, helping to drive home the methods taught in class and how to interpret your work. If you’re interested in developing a strong understanding of the fundamentals of machine learning in a rigorous format, this class is for you. I also couldn’t recommend Luke as an instructor more. He’s awesome! I was also was very pleased with my choice of the R class. R reduces a lot of the friction in model implementation, which allowed me to focus on developing an understanding of the concepts and interpreting results.

Lukasz

This course covers major R machine learning topics; it is intense, and you will learn a lot if you keep up with the pace. Instructor Shu Yan is great at explaining complicated statistical concepts/formulas and translate them into R coding techniques. Course materials, in-class practices, and homework assignment are helpful regarding learning and future references. I would recommend this course to anyone who is interested in data science/machine learning but doesn't know much about this field. It will be a good start for you if you plan to work in this area. It certainly helped me understand a lot about data science and improved my R coding skills. What I learned from this course is worth the money I paid and the effort I put in.

Tingyan Zheng

I took Machine Learning with R and Hadoop data engineering classes in 2015. They are all well-structured classes with extensive information coverage and concrete learning process design. All the techniques been told in the class are very practical and can be applied to work very fast. In addition, it is also a great opportunity to build your "data science" fellow network because all your classmates are "Pro" in this domain with a lot of wonderful industry experiences to share. I would definitely recommend NYC Data Science Academy to my friend!

Mark Li

As the business world becomes increasingly data-driven, the Data Sciences classes at NYC Data Sciences Academy are invaluable to driving career success, not only for actual data science practitioners, but those who collaborate with them day-to-day to execute on insights to be gleaned from data sciences. I just completed the Intermediate level Data Sciences with R class and have immediately benefited from the ability to understand the different type of advanced analytic techniques that are available to help my clients with their business issues, to better communicate and collaborate with our Data Sciences team on a tactical level and then to take their output and accurately translate it into our clients’ business language. The course was comprehensive and Vivian brings a lot of passion and dedication to the class and ensuring her students’ success.

Margaret Hung

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

I studied mechanical engineering and physics for my undergrad at a top university and work in product management with a focus on search. I took this class to satisfy a personal interest in the subject matter and familiarize myself enough with the fundamentals of machine learning to be able to explore the field more deeply on my own. I was also motivated by a career interest: the subject matter is highly relevant to my domain, and I feel that developing an understanding of the concepts and how to deploy them myself will make me better at my job long-term. Prior to enrolling in the class, I spent roughly 8-10 hours learning R and felt sufficiently prepared (I had some previous programming experience). In the end I was extremely happy with this class (Machine Learning in R on Saturdays, 8 hrs at a time). The curriculum and content were excellent, the instructor, Luke, was fantastic and the assignments were challenging and informative. I felt the course did a really great job of driving home the core fundamentals of each subject with a focus on statistics, mathematical theory, derivations and best practices. We covered a LOT of material, yet the material had a lot of depth. I thought the sequencing of the subject matter was very well thought out as well. The class was demanding and had the caliber of a graduate-level course. The course also struck a very nice balance between theory and implementation. After learning about a new model, we would immediately implement it in class using R on our own machines. Luke did a particularly great job at relating the implementation back to the concepts and teaching us how to interpret outcomes of our analyses (I can’t stress enough how important this latter point was for me). He has a really strong grasp of the subject matter, he’s very patient and responsive to questions, offers a lot of insightful commentary on the theory, implementations, and best practices, and he cares about his students a lot. The homework assignments complement the class nicely as well, helping to drive home the methods taught in class and how to interpret your work. If you’re interested in developing a strong understanding of the fundamentals of machine learning in a rigorous format, this class is for you. I also couldn’t recommend Luke as an instructor more. He’s awesome! I was also was very pleased with my choice of the R class. R reduces a lot of the friction in model implementation, which allowed me to focus on developing an understanding of the concepts and interpreting results.

Lukasz

This course covers major R machine learning topics; it is intense, and you will learn a lot if you keep up with the pace. Instructor Shu Yan is great at explaining complicated statistical concepts/formulas and translate them into R coding techniques. Course materials, in-class practices, and homework assignment are helpful regarding learning and future references. I would recommend this course to anyone who is interested in data science/machine learning but doesn't know much about this field. It will be a good start for you if you plan to work in this area. It certainly helped me understand a lot about data science and improved my R coding skills. What I learned from this course is worth the money I paid and the effort I put in.

Tingyan Zheng

I took Machine Learning with R and Hadoop data engineering classes in 2015. They are all well-structured classes with extensive information coverage and concrete learning process design. All the techniques been told in the class are very practical and can be applied to work very fast. In addition, it is also a great opportunity to build your "data science" fellow network because all your classmates are "Pro" in this domain with a lot of wonderful industry experiences to share. I would definitely recommend NYC Data Science Academy to my friend!

Mark Li

As the business world becomes increasingly data-driven, the Data Sciences classes at NYC Data Sciences Academy are invaluable to driving career success, not only for actual data science practitioners, but those who collaborate with them day-to-day to execute on insights to be gleaned from data sciences. I just completed the Intermediate level Data Sciences with R class and have immediately benefited from the ability to understand the different type of advanced analytic techniques that are available to help my clients with their business issues, to better communicate and collaborate with our Data Sciences team on a tactical level and then to take their output and accurately translate it into our clients’ business language. The course was comprehensive and Vivian brings a lot of passion and dedication to the class and ensuring her students’ success.

Margaret Hung

More Alumni Comments

Campus Location

500 8th Ave Suite 905, New York, NY 10018

Nearby Subways

1 2 3 34th, Penn Station

A C E 34th, Penn Station

N Q R B D F M 34th, Herald Square

Detailed Directions

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

Instructor

Kathy Liu

Instructor

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.