R Ultimate 2024: R for Data Science and Machine Learning

R Basics, Data Science, Statistical Machine Learning models, Deep Learning, Shiny and much more (All R code included)

4.71 (373 reviews)

Udemy

platform

English

language

Data Science

What you will learn

learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning

learn R basics (data types, structures, variables, and more)

learn R programming (writing loops, functions, and more)

data im- and export

basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)

data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)

advanced data manipulation (outlier detection, missing data handling, regular expressions)

regression models (create and apply regression models)

model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)

regularization (What is regularization? How can you apply it?)

classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)

association rules (learn the apriori model)

clustering (kmeans, hierarchical clustering, DBscan)

dimensionality reduction (factor analysis, principal component analysis)

Reinforcement Learning (upper confidence bound)

Deep Learning (deep learning for multi-target regression, binary and multi-label classification)

Deep Learning (learn image classification with convolutional neural networks)

Deep Learning (learn about Semantic Segmentation)

Deep Learning (Recurrent Neural Networks, LSTMs)

More on Deep Learning, e.g. Autoencoders, pretrained models, ...

R/Shiny for web application development and deployment

Course Gallery

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R Ultimate 2024: R for Data Science and Machine Learning

What you will learn

learn all aspects of R from Basics, over Data Science, to Machine Learning and Deep Learning

learn R basics (data types, structures, variables, and more)

learn R programming (writing loops, functions, and more)

data im- and export

basic data manipulation (piping, filtering, aggregation of results, data reshaping, set operations, joining datasets)

data visualisation (different packages are learned, e.g. ggplot, plotly, leaflet, dygraphs)

advanced data manipulation (outlier detection, missing data handling, regular expressions)

regression models (create and apply regression models)

model evaluation (What is underfitting and overfitting? Why is data splitted into training and testing? What are resampling techniques?)

regularization (What is regularization? How can you apply it?)

classification models (understand different algorithms and learn how to apply logistic regression, decision trees, random forests, support vector machines)

association rules (learn the apriori model)

clustering (kmeans, hierarchical clustering, DBscan)

dimensionality reduction (factor analysis, principal component analysis)

Reinforcement Learning (upper confidence bound)

Deep Learning (deep learning for multi-target regression, binary and multi-label classification)

Deep Learning (learn image classification with convolutional neural networks)

Deep Learning (learn about Semantic Segmentation)

Deep Learning (Recurrent Neural Networks, LSTMs)

More on Deep Learning, e.g. Autoencoders, pretrained models, ...

R/Shiny for web application development and deployment

Course Gallery

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