Machine Learning Fundamentals
Get an introduction to essential machine learning concepts and frameworks in R.
Machine Learning Fundamentals is a free R Academy lesson on CoddyKit. This is lesson 3 of 3. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 3 lessons in total.
Introduction to Machine Learning in R
Machine learning enables computers to learn patterns from data and make predictions. R provides multiple libraries for machine learning, including caret and randomForest.
Loading the Caret Package
The caret package simplifies machine learning workflows in R.
library(caret)
data(iris)Splitting Data into Train and Test Sets
Splitting data ensures proper model evaluation by training and testing separately.
set.seed(123)
trainIndex <- createDataPartition(iris$Species, p=0.8, list=FALSE)
trainData <- iris[trainIndex,]
testData <- iris[-trainIndex,]Building a Decision Tree Model
Decision trees classify data by splitting features at decision points.
library(rpart)
model <- rpart(Species ~ ., data=trainData)
plot(model)
text(model)Training a Random Forest Model
Random forests improve decision trees by combining multiple models.
library(randomForest)
rf_model <- randomForest(Species ~ ., data=trainData)
print(rf_model)Making Predictions
Use predict() to make predictions on new data.
predictions <- predict(rf_model, newdata=testData)Evaluating Model Accuracy
Accuracy measures how well the model performs on test data.
confusionMatrix(predictions, testData$Species)Hyperparameter Tuning
Use train() to optimize model parameters for better performance.
tuned_model <- train(Species ~ ., data=trainData, method='rf', tuneLength=3)Summary
In this lesson, you learned:
- How to split data into training and testing sets.
- How to build decision tree and random forest models.
- How to evaluate model performance and tune hyperparameters.
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Frequently Asked Questions
Is the “Machine Learning Fundamentals” lesson free?
Yes — any 3 lessons of the R Academy learning path, including “Machine Learning Fundamentals”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 3 lessons in total.
What will I learn in “Machine Learning Fundamentals”?
Get an introduction to essential machine learning concepts and frameworks in R. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 3 of 3.
How long does the “Machine Learning Fundamentals” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Regression Models
- Time Series Analysis
- Machine Learning Fundamentals