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Applied Machine Learning in R

Get the essential machine learning skills and use them in real life situations

  1. Topics
  2. Development
  3. R Programming & Statistics

Applied Machine Learning in R

InstructorBogdan Anastasiei
Duration8h 11m
Students24.2K
Rating4.7 (294)
Sponsored
Price
$12.99
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Comidoc Analysis

Efficient R-based Machine Learning Implementation

Strengths

Broad Algorithmic Coverage

The curriculum spans diverse techniques including penalized regression, support vector machines, and tree-based methods like bagging and boosting.

Direct Instructional Style

Lessons are described as succinct and easy to follow, providing well-annotated R scripts for practical use.

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Limitations

Limited Theoretical Depth

Learner feedback suggests a lack of detailed theoretical explanation regarding model evaluation and specific techniques like regularization.

Best suited to

  • Learners seeking to implement ML algorithms in R quickly
  • Data analysts needing a practical refresher on R-based techniques

Less suited to

  • Students requiring deep mathematical or theoretical foundations
  • Those looking for extensive statistical inference explanations

Comidoc Score

6.2/10

Worth considering
Defined audience

Comidoc verdict

The learning path moves from fundamental supervised techniques, such as ridge and logistic regression, into more complex areas like decision trees and unsupervised cluster analysis. The curriculum is structured to facilitate the practical application of these models using R-specific packages.

Instruction provides a clear procedural roadmap for running analyses but lacks the mathematical depth required for a rigorous statistical understanding. This makes the course highly efficient for those who want to execute code quickly but less useful for those needing to understand the underlying mechanics of every algorithm.

This trade-off is ideal for practitioners or students with some existing statistical knowledge who need a direct, practical guide to implementing machine learning workflows in R.

Score breakdown

Curriculum depth
8.0

The curriculum covers a wide range of topics from regression to tree-based methods and unsupervised learning, though learners noted a lack of depth in theoretical explanations.

Applied learning
3.5

Instruction includes practical R script implementation and model validation, though explicit project or lab counts are not explicitly detailed in the metadata.

Clarity & experience
6.5

Learners report that the lectures are succinct and easy to follow, aided by downloadable R scripts.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
6.5

The curriculum aligns well with the stated goals of data analysts and researchers using R, provided they have basic statistical knowledge.

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