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Machine Learning Scientist in R

Updated 03/2026
A machine learning scientist researches new approaches and builds machine learning models.
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RMachine Learning65 hr5,137

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Track Description

Machine Learning Scientist in R

Master the essential skills to land a job as a machine learning scientist! You'll augment your R programming skillset with the toolbox to perform supervised and unsupervised learning. You'll learn how to process data for modeling, train your models, visualize your models and assess their performance, and tune their parameters for better performance. In the process, you'll get an introduction to Bayesian statistics, natural language processing, and Spark.

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Supervised Learning in R: Classification

    In this course you will learn the basics of machine learning for classification.

  • Course

    2

    Supervised Learning in R: Regression

    In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

  • Course

    3

    Feature Engineering in R

    Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

  • Course

    4

    Unsupervised Learning in R

    This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

  • Course

    7

    Cluster Analysis in R

    Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

  • Course

    10

    Machine Learning with Tree-Based Models in R

    Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

  • Skill Assessment

    bonus

    Machine Learning Fundamentals in R

  • Course

    12

    Dimensionality Reduction in R

    Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.

Machine Learning Scientist in R
16 Courses
Track
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FAQs

Is this Track suitable for beginners?

No, this track is not suitable for absolute beginners. This track is designed for students who are already familiar with R programming and have a basic understanding of machine learning. Before starting this track, we recommend that users should have a basic understanding of statistics, linear algebra, and calculus.

What is the programming language of this Track?

This track uses R programming language. R is a popular open-source programming language for data analysis and statistical computing.

Which jobs will benefit from this Track?

This track is best suited for those who want to land a job as a machine learning scientist. It will help users learn the essential skills needed to work as a data scientist, research scientist, or AI engineer. Beyond this, people wanting to gain a more in-depth knowledge of machine learning and R programming can also make use of this track.

How will this Track prepare me for my career?

This track will equip you with the in-depth knowledge of R programming and machine learning algorithms. You will learn about supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.

How long does it take to complete this Track?

This track is self-paced so users can spend as long or as little time as they like working through exercises and courses. Generally, it takes around 65 hours to go through the entire track, as it consists of multiple courses.

What's the difference between a skill track and a career track?

A skill track focuses on a specific technique or technology related to a certain job. Whereas, a career track focuses on a broader set of skills and expertise that can help in a career as a whole, such as a data scientist or software developer.

What topics will I learn during this track?

This track covers topics such as machine learning algorithms using R, supervised and unsupervised learning, data processing for modeling, training and visualizing models, assessing performance, tuning parameters, Bayesian statistics, natural language processing, and Spark.

Do I need knowledge of machine learning prior to taking this track?

No, it is not necessary to have prior knowledge of machine learning prior to taking this track. However, this track is best suited for those who have a basic understanding of R programming and machine learning concepts.

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