Overview
Context of the Dimensionality Reduction in R course at Data Camp
Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages.
Firstly, you will have a look at t-SNE, an algorithm that performs non-linear dimensionality reduction. Then, you will also explore some useful characteristics of dimensionality reduction to apply in predictive models. Finally, you will see the application of GLRM to compress big data (with numerical and categorical values) and impute missing values. Are you ready to start compressing high dimensional data?
Programme Structure
Chapters
- Using t-SNE with Predictive Models
- Generalized Low Rank Models (GLRM)
- Advanced Dimensionality Reduction
- t-SNE
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Information Technology (IT) Computer Sciences Data Science & Big Data View 746 other Short Courses in Computer Sciences in United StatesAcademic 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
PREREQUISITES
- Unsupervised Learning in R
Tuition Fee
To always see correct tuition fees
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International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days.
Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing
Funding
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