Machine learning and data science are becoming more and more popular in today’s world, and for good reason. These techniques allow us to make predictions, classify data, and understand patterns in data that we would not be able to discern otherwise. In this article, we will be discussing how to use machine learning for …

# Month: May 2020

Machine learning and data science are powerful tools that can help us make predictions and understand patterns in large sets of data. In this article, we will explore how to use these tools with a popular dataset from the UCI Machine Learning Repository: the mushroom dataset. This dataset contains information about different types of …

Machine learning and data science are two rapidly growing fields that are used to analyze and make predictions based on large sets of data. One of the most popular datasets used for machine learning and data science is the Mushroom dataset from UCI. This dataset contains information about different types of mushrooms and their characteristics, …

Machine learning is a powerful tool for data analysis and prediction. It involves training a model on a dataset, and then using that model to make predictions on new data. One of the most popular machine learning algorithms is the random forest algorithm, which is a type of decision tree algorithm. A decision …

Machine Learning Classification in R using Support Vector Machine (SVM) with IRIS Dataset is a popular technique used in Data Science to classify data into different categories. SVM is a supervised learning algorithm that can be used for both classification and regression tasks. The main idea behind SVM is to find a hyperplane that …

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. In this article, we will discuss how to use the QDA (Quadratic Discriminant Analysis) model for classification in R using the IRIS dataset from the UCI machine learning repository. The IRIS dataset is a well-known dataset in …

Machine learning is a method of teaching computers to learn from data without being explicitly programmed. One of the most commonly used algorithms for classification tasks is the Linear Discriminant Analysis (LDA) algorithm. In this article, we will be discussing how to use LDA for classification in R using the IRIS dataset from …

## Deep Learning in R with Dropout Layer | Data Science for Beginners | Regression | Tensorflow | Keras

Deep learning is a powerful machine learning technique that allows for the creation of complex models to solve difficult problems. In this article, we will be discussing how to use dropout layers in R to improve the performance of a deep learning model for regression tasks. Dropout is a regularization technique that is used …

## Deep Learning in R | Data Science for Beginners | Tensorflow | Keras | House Price Data | Regression

Deep learning is a subset of machine learning that involves training artificial neural networks to perform tasks such as image or speech recognition, natural language processing, and predictive modeling. In this article, we will discuss how to use deep learning in R to perform regression on a housing price dataset using the Tensorflow …

Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. In R, there are many libraries available for machine learning, such as caret, randomForest, and nnet. One of the most popular datasets for machine learning is the Boston house price dataset, which is available in the …

Machine learning is a technique that allows computers to learn from data and make predictions or decisions without being explicitly programmed. In this article, we will discuss how to use the Random Forest algorithm for regression tasks in R with the Boston House Data from the UCI Machine Learning Repository. First, we …

Machine learning is a powerful tool that allows us to make predictions and analyze data using a variety of algorithms. In this article, we will focus on using the XGBoost algorithm for regression tasks in R. We will be using the Boston Housing Price dataset from the UCI repository, and the CARET …