R Machine Learning Libraries
Machine Learning Engineer
The average salary for Machine Learning Engineer is $157,315 / year according to Indeed.com
R libraries for machine learning offer various functions for tasks like data preprocessing, feature engineering, model building, and evaluation. They are well-documented and have active communities, making them user-friendly and easy to learn.
Machine Learning Scientist with R
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Machine Learning Specialization
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Mathematics for Machine Learning: Linear Algebra
This course is jam-packed with lessons and exercises to help you develop an intuitive understanding of linear algebra concepts such as vectors, matrices, basis (linear algebra), eigenvalues, eigenvectors, and transformation matrices, among others.
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R Programmer
Caret
Caret is a package for R that provides a set of functions to train and evaluate machine learning models. It provides a common interface for a variety of machine learning algorithms, making it easy to compare and evaluate different algorithms. Caret also provides a variety of functions for data preprocessing, model selection, and performance evaluation.
MLR
MLR is a package for R that provides a unified interface for machine learning algorithms from different R packages. It provides a variety of functions for training and evaluating machine learning models, as well as functions for data preprocessing, model selection, and performance evaluation. MLR also includes a number of machine learning algorithms that are not available in other R packages.
H2O.ai
H2O.ai is an open-source, distributed machine learning platform that makes it easy for businesses of all sizes to build and deploy AI models. It offers a variety of features for data preparation, machine learning, and model deployment, as well as a powerful AutoML platform that can automatically select and tune machine learning algorithms for your data.
Keras
Keras is a high-level API for building and training machine learning models. It is built on top of TensorFlow and provides a simple and easy-to-use interface for building and training neural networks. Keras is a good choice for beginners and for developers who want to quickly prototype and deploy machine learning models.
R is a programming language that is widely used for statistical computing and graphics. It provides a variety of libraries and packages specifically designed for machine learning tasks. These libraries offer a range of algorithms and tools to analyze and interpret data, making it easier to build predictive models and make data-driven decisions.
One popular library in R for machine learning is caret (Classification And REgression Training). It provides a unified interface to various machine learning algorithms, making it easier to compare and select the best model for a given task. With caret, you can preprocess data, tune hyperparameters, and evaluate model performance using cross-validation techniques.
Another widely used library is randomForest, which implements the random forest algorithm. Random forests are an ensemble learning method that combines multiple decision trees to make predictions. This library is particularly useful for tasks such as classification and regression, where it can handle both categorical and continuous variables.
For deep learning tasks, the keras library in R provides a high-level interface to the powerful TensorFlow library. Keras allows you to build and train deep neural networks with ease, making it suitable for tasks such as image recognition, natural language processing, and time series forecasting.
In addition to these libraries, R also offers packages for specific machine learning tasks. For example, the e1071 package provides support for support vector machines (SVM), a popular algorithm for classification and regression. The glmnet package offers tools for fitting generalized linear models with regularization, which is useful for handling high-dimensional data.
Overall, R machine learning libraries provide a wide range of tools and algorithms to analyze and interpret data. Whether you need to build predictive models, classify data, or perform deep learning tasks, these libraries can help you make sense of your data and make informed decisions.
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