Tag Archives: Machine Learning in R Tutorials

Data Analytics – GGPlot Title, Subtitle and Caption

Hits: 174 GGPLOT TITLE, SUBTITLE AND CAPTION   This article describes how to add and change a main title, a subtitle and a caption to a graph generated using the ggplot2 R package. We’ll show also how to center the title position, as well as, how to change the title font size and color. In this R graphics tutorial, you will learn how to: Add titles …

Data Science and Machine Learning for Beginners in R – Tensorflow and Keras with Dropout layers using Mushroom Dataset

Hits: 842   TensorFlow and Keras are two popular open-source tools used for machine learning and deep learning. They are often used together to build and train neural networks, which are a type of model that can be used for tasks such as image recognition, natural language processing, and more. One important technique used in …

Data Science and Machine Learning for Beginners in R – Tensorflow and Keras using Mushroom Dataset

Hits: 258 Tensorflow is an open-source software library developed by Google for machine learning. It is a powerful tool that can be used to build and train neural networks. Keras is a high-level library that runs on top of Tensorflow and is used to simplify the process of building and training neural networks. Together, Tensorflow …

Data Science and Machine Learning for Beginners in R – Boosting Ensembles with Grid Search using Mushroom Dataset

Hits: 119   Boosting is another ensemble learning method that is used to improve the performance of machine learning models. Like bagging, boosting combines the predictions of multiple models, but it does so in a different way. Instead of generating multiple subsets of the data and training a model on each subset, boosting trains a …

Data Science and Machine Learning for Beginners in R – Random Forest with Grid Search using Mushroom Dataset

Hits: 210   Random Forest is a type of ensemble learning algorithm that can be used for both classification and regression tasks. It works by building multiple decision trees and combining their predictions to make a final prediction. One of the advantages of Random Forest is that it can help to reduce overfitting, which is …

Data Science and Machine Learning for Beginners in R – Bagging Ensemble Algorithms using Mushroom Dataset

Hits: 158   Ensemble learning is a powerful technique in machine learning that combines the predictions of multiple models to improve the overall performance of a system. One popular ensemble method is called bagging, which stands for Bootstrap Aggregating. Bagging is a technique that generates multiple subsets of the data, and then trains a model …

Data Science and Machine Learning for Beginners in R – Naive Bayes Algorithm using Mushroom Dataset

Hits: 131 In this Applied Machine Learning & Data Science Coding Recipe, the reader will find the practical use of applied machine learning and data science in Python and R programming. Data Science and Machine Learning for Beginners in R – Naive Bayes Algorithm using Mushroom Dataset. What should I learn from this Applied Machine …

Data Science and Machine Learning for Beginners in R – KNN Algorithm using Mushroom Dataset

Hits: 160 In this Applied Machine Learning & Data Science Coding Recipe, the reader will find the practical use of applied machine learning and data science in Python and R programming. Data Science and Machine Learning for Beginners in R – KNN Algorithm using Mushroom Dataset. What should I learn from this Applied Machine Learning …

Data Science and Machine Learning for Beginners in R SVM using Mushroom Dataset

Hits: 55 Machine learning and data science are powerful tools that can help us make predictions and gain insights from large amounts of data. One way to learn about these techniques is by using them to analyze a dataset. In this article, we will explore how to use support vector machines (SVMs) in R to …