Machine Learning with R

Begin by configuring your development environment, installing the studio setup, and selecting the right interpreter, while verifying system requirements like Windows and RAM for your console-based workflow.

Learn to set up and use a console and the studio interface for machine learning with R, configure the development environment, and manage packages for a smooth setup.

Explore how an integrated development environment and console interface with a coeditor streamline software development, debugging, and package management for machine learning with R.

Explore the data mining process for machine learning with emphasis on business understanding, data understanding, data operation and cleaning, modeling with classification and regression, and evaluation to select accurate models.

Before performing statistics, download the dataset from the UCSB site to begin the machine learning with R workflow and use the research asset as guidance.

Read a dataset in R, perform descriptive statistics, and summarize the data using the iris dataset as an example, then set up the data structure to run analyses.

Explore how linear regression and neural network base models use datasets and variables to predict species. Learn about formulas, training, and generating predictions from the model.

Train and evaluate machine learning models, and loop back to business understanding or data processing to improve accuracy and precision by adjusting model settings and handling missing values.

Train a linear regression on a dataset in machine learning with R, splitting 75% for training and 25% for testing, use alpha and beta to predict prices from engine size.

Learn how to build linear regression in R by fitting a line to data and interpreting the relationship to predict outcomes.

Learn to create a new dataset, view data, and run linear regression to draw a regression line and predict outcomes for new data.

Explore how k-means clustering groups data points into two clusters by iteratively updating centers and assigning points based on distances, and repeat until convergence.

Learn to run kmeans clustering in R by selecting the first four columns of new data, choosing a cluster count such as 15, and evaluating the resulting clusters.

Compute pairwise distances between data objects, select the nearest pairs, and iteratively merge them to form clusters using distance matrices.

Learn agglomeration clustering in R using hierarchical methods to structure data, cross-training across clusters, and forecasting outcomes.

Learn how the ID3 decision tree algorithm uses entropy and information gain to select attributes and build a tree. See how rules classify data based on these choices.

Build a decision tree in R by importing the proper libraries and applying a 70/30 train-test split to evaluate the model.

Train and run a decision tree, view its rules, and control the process across settings.

Explore how decision trees enable predictions using the ID3 algorithm and compare classification and regression trees. Review the prediction results from previous runs to understand the tree's performance and usage.

Explore k-nearest neighbors classification by calculating distances, selecting the top k, and predicting the most frequent class. See how choosing k affects accuracy using training and test data.

Learn to train KNN in R by installing the required package, configuring k and train control, and running the workflow to view results and obtain accuracy.

Explore knn in R for prediction tasks within machine learning, applying the k-nearest neighbors approach to real data in R.

Learn how the naive Bayes classification algorithm uses the independence assumption and Bayes' rule to compute class probabilities from frequency-based, categorical data, with a practical probability calculation example.

Build and evaluate a naive bayes model in R by installing libraries, preparing a trainset, training the model, and generating predictions from new data.

Explore how a neuron processes inputs with weights and bias, applies an activation function, and uses backpropagation and iterations to train a multilayer perceptron for classification.

Learn to install and load libraries in R, train a neural network for species prediction, and generate probability-based predictions for new data.

Learn how to choose the right machine learning algorithm for data mining tasks—classification, regression, clustering, and dimensionality reduction—using a cheat sheet from Python machine learning libraries.

Learn to evaluate regression models in R using R-squared, residuals, SSE, and SST, and assess classification performance with accuracy and precision from confusion matrix concepts.

Explore evaluating classification models in R by building neural networks and decision trees, then use confusion matrices to assess accuracy, precision, sensitivity, and specificity on train and test sets.

Learn how to evaluate a regression model in R by using squared residuals and related metrics to assess accuracy and compare model performance.