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In this course you will learn the basics of machine learning for classification.
In this course you will learn the basics of machine learning for classification.
Track
A machine learning scientist researches new approaches and builds machine learning models.
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Make progress on the go with our mobile courses and daily 5-minute coding challenges.
Course
In this course you will learn the basics of machine learning for classification.
In this course you will learn the basics of machine learning for classification.
Course
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Course
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.
Course
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Course
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.
Machine Learning Scientist in R
Track
Complete
VP, Data Science at DataRobot
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.
This track uses R programming language. R is a popular open-source programming language for data analysis and statistical computing.
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.
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.
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.
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.
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.
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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