Last Updated on August 15, 2020 As a developer you can pick-up R super fast. If you are already a developer, you don’t need to know much about a new language to be able to reading and understanding code snippets and writing your own small scripts and programs. In this post you will discover the basic syntax, data structures and control structures that you need to know to start reading and writing R scripts. Kick-start your project with my new […]

Last Updated on August 22, 2019 You must become intimate with your data. Any machine learning models that you build are only as good as the data that you provide them. The first step in understanding your data is to actually look at some raw values and calculate some basic statistics. In this post, you will discover how you can quickly get a handle on your dataset with descriptive statistics examples and recipes in R. These recipes are perfect for you if […]

Last Updated on August 22, 2019 You must understand your data to get the best results from machine learning algorithms. Data visualization is perhaps the fastest and most useful way to summarize and learn more about your data. In this post you will discover exactly how you can use data visualization to better understand or data for machine learning using R. This post is perfect if you are a developer and are just starting using R for machine learning, or […]

Last Updated on December 13, 2019 What algorithm should you use on your dataset? This is the most common question in applied machine learning. It’s a question that can only be answered by trial and error, or what I call: spot-checking algorithms. In this post you will discover how to spot check algorithms on a dataset using R. Including the selection of test options, evaluation metrics, and algorithms. You can use the code in this post as a template for spot […]

Last Updated on October 8, 2019 Do you want to do machine learning using R, but you’re having trouble getting started? In this post you will complete your first machine learning project using R. In this step-by-step tutorial you will: Download and install R and get the most useful package for machine learning in R. Load a dataset and understand it’s structure using statistical summaries and data visualization. Create 5 machine learning models, pick the best and build confidence that the accuracy is reliable. […]

Last Updated on July 31, 2020 It is difficult to find a good machine learning algorithm for your problem. But once you do, how do you get the best performance out of it. In this post you will discover three ways that you can tune the parameters of a machine learning algorithm in R. Walk through a real example step-by-step with working code in R. Use the code as a template to tune machine learning algorithms on your current or […]

Last Updated on August 22, 2019 Ensembles can give you a boost in accuracy on your dataset. In this post you will discover how you can create three of the most powerful types of ensembles in R. This case study will step you through Boosting, Bagging and Stacking and show you how you can continue to ratchet up the accuracy of the models on your own datasets. Kick-start your project with my new book Machine Learning Mastery With R, including […]

Last Updated on August 22, 2019 You need to be able to load data into R when working on a machine learning problem. In this short post, you will discover how you can load your data files into R and start your machine learning project. Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples. Let’s get started. Load Your Machine Learning Data Into RPhoto by […]

Last Updated on August 15, 2020 You need standard datasets to practice machine learning. In this short post you will discover how you can load standard classification and regression datasets in R. This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R. It is invaluable to load standard datasets in R so that you can test, practice and experiment with machine […]