Building Our First Model - Introduction to Text Analytics with R Part 4
Learn to build and evaluate a text classification model using R, covering tokenization, cross-validation, parallel processing, and decision tree training for optimal accuracy in text analytics.
Data Pipelines - Introduction to Text Analytics with R Part 3
Explore text analytics pre-processing techniques using R, including tokenization, stop word removal, and stemming. Learn to create document-frequency matrices for machine learning models.
Introduction to Text Analytics with R - Overview
Explore text analytics with R: tokenization, feature engineering, classification models. Learn data cleaning, analysis, and visualization using a spam dataset. Practical introduction for aspiring data scientists.
Introduction to R Programming for Excel Users
Learn to apply Excel skills in R programming for data analysis, visualization, and advanced techniques. Discover how Excel concepts translate to R, enabling powerful data manipulation and insights.
Intro to R Visualizations in Microsoft Power BI - R Visualizations in Power BI
Learn to create dynamic R visualizations in Power BI, enhancing data exploration and analysis capabilities. Hands-on introduction covers ggplot2, data preprocessing, and integrating R with Power BI's interface and controls.
Introduction to Event Log Mining with R
Learn to mine event logs using R and ProM, covering scenarios, benefits, data requirements, analysis techniques, and feature creation for machine learning models in various data sources.
Scale R to Big Data with Hadoop & Spark
Learn to scale R for big data using Hadoop and Spark. Set up a Spark cluster, wrangle HDFS data, and build/deploy machine learning models using Microsoft R Server and distributed processing libraries.
Feature Exploration, Visualization, and Predictive Modeling with R and Azure ML
Explore predictive analytics using the Titanic dataset. Learn feature exploration and visualization with R, then build models using Azure ML Studio for efficient experimentation.
Making a CV with R and RStudio: Professional and Semantic
Learn to create professional CVs using R and RStudio with the vitae package. Explore 6 templates and leverage markdown for customization in this efficient, programmer-friendly approach to resume building.
R in Java - Performance and Implementation Challenges
Explore R integration in Java, covering performance, memory allocation, and language features. Learn about specialized interpreters and data types for efficient R-Java interoperability.
Learn R Programming in 7 Hours - Statistical Programming and Data Analysis
Comprehensive R programming tutorial covering installation, environment, data types, structures, functions, loops, and advanced topics for statistical analysis and data manipulation.
Introduction to Lasso and Ridge Regression Models in R
Master linear regression regularization techniques through Lasso and Ridge methods in R, focusing on preventing overfitting and improving model reliability for more accurate predictions and estimations.
Machine Learning in R - Repurpose Machine Learning Code for New Data
Learn to adapt machine learning code in R for new datasets. Explore data understanding, visualization, and model building techniques using the DHFR dataset as an example.
Word Embedding with Keras - A RStudio Tutorial
Learn word embedding techniques using Keras in RStudio. Covers theory, concepts, and practical implementation, including Keras basics and machine learning fundamentals for natural language processing.
Principal Component Analysis - PCA Clearly Explained - 2015
Clear explanation of Principal Component Analysis, covering dimensions, variance, loading scores, and key concepts for interpreting PCA plots in RNA-seq data analysis.
Sentiment Analysis in R with Sentimentr - Handling Negation and Valence Shifters
Explore sentiment analysis in R using {sentimentr}, handling negation and valence shifters for accurate text data analysis of emotions and profanity.
Predictive Modeling in R With Tidymodels and NFL Attendance
Learn to train predictive models using R's tidymodels framework and NFL game attendance data. Explore data splitting, model building, evaluation, and results visualization techniques.
Topic Modeling with R and Tidy Data Principles
Explore topic modeling in R using tidytext and stm packages, applied to Sherlock Holmes stories. Learn data preparation, text tidying, and model implementation techniques.
Topic Modeling for Spice Girls Lyrics
Explore topic modeling using R to analyze Spice Girls lyrics, uncovering hidden themes and patterns in their music through unsupervised machine learning techniques.
Model Canadian Wind Turbine Capacity With Decision Trees and Tidymodels
Learn to predict Canadian wind turbine capacity using decision trees and tidymodels, with hands-on data preparation, model tuning, and result interpretation techniques.
Build a Predictive Text Model for Avatar: The Last Airbender with Tidymodels
Predict Avatar: The Last Airbender speakers using text features and tidymodels in R. Learn model-agnostic variable importance and handle class imbalance in this data analysis tutorial.