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Overview
Context
In this Machine Learning Fundamentals in R course offered by Data Camp you will learn how to process data for modeling, how to train your models, how to visualize your models and assess their performance, and how to tune their parameters for better performance.
What you will do during this course:
- You will learn the basics of machine learning for classification.
- You will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
- This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
- This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
- Learn to streamline your machine learning workflows with tidymodels.
- Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Programme Structure
Courses include:
- Supervised Learning in R: Classification
- Supervised Learning in R: Regression
- Bonus: Predict Future Sales of Fast-Food Menu Items
- Unsupervised Learning in R
- Machine Learning with caret in R
- Modeling with tidymodels in R
- Machine Learning with Tree-Based Models in R
- Bonus: Machine Learning Fundamentals in R
Key information
Duration
- Part-time
- 3 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- New York City, United States
Disciplines
Machine Learning View 213 other Short Courses in Machine Learning in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- There are no prerequisites for this track
- This track is suitable for beginners. Working through this track, users will gain a comprehensive understanding of the basics of machine learning such as how to process data for modeling, how to train models, evaluate their performance, and tune their parameters for better performance.
- This track is beneficial for individuals interested in jobs such as data science, machine learning engineer, and artificial intelligence specialist.
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions
Funding
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