Udemy – R Ultimate 2024: R for Data Science and Machine Learning 2024-5

Apr 18, 2025 - 18:53

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R Ultimate 2024: R for Data Science and Machine Learning. This comprehensive course will help you get started with R, from the basics to the most advanced techniques for Data Science and Machine Learning. We’ll start by learning about R data types and structures, and move on to advanced topics like Deep Learning and developing web applications with R/Shiny. In this course, you’ll not only learn the concepts, but also hone your skills through hands-on exercises and projects. Every line of code is explained in detail, and you’ll learn how and why to use it.

What you will learn:

Learn R completely: From basics to Data Science, Machine Learning, and Deep Learning
R Basics: Data Types, Structures, Variables, etc.
R Programming: Writing Loops, Functions, and More
Importing and exporting data: data im- and export
Basic data manipulation: piping, filtering, aggregating results, data transformation, set operations, merging datasets
Data visualization: using various packages such as ggplot, plotly, leaflet, dygraphs
Advanced data manipulation: outlier detection, missing data handling, regular expressions
Regression Models: Create and apply regression models
Model evaluation: underfitting and overfitting, data splitting into training and testing, resampling techniques
Regularization: The concept of regularization and how to apply it
Classification models: various algorithms, logistic regression, decision trees, random forests, support vector machines
Association rules: apriori model
Clustering: kmeans, hierarchical clustering, DBscan
Dimensionality reduction: factor analysis, principal component analysis
Reinforcement Learning: upper confidence bound
Deep Learning: multi-objective regression, binary and multi-label classification
Deep Learning: Image classification with convolutional neural networks
Deep Learning: Semantic Segmentation
Deep Learning: Recurrent Neural Networks, LSTMs
Advanced Deep Learning: Autoencoders, pre-trained models, etc.
R/Shiny: Web application development and deployment