Julia Machine Learning Guide — MLJ, Flux, and Data Science Tools

In this tutorial, you will learn about Julia Machine Learning Guide. We cover key concepts, practical examples, and best practices to help you master this topic.

Julia machine learning offers MLJ.jl (unified interface to 150+ models), Flux.jl (neural networks with GPU support), GLM.jl (statistical models), and DecisionTree.jl -- with Pipeline and Resampling for train/test splits and cross-validation.

MLJ for Classical ML

using MLJ
using DataFrames

# Load data
X, y = @load_iris

# Split data
train, test = partition(eachindex(y), 0.8, shuffle=true)

# Load model
KNNClassifier = @load KNNClassifier pkg=NearestNeighbor
model = KNNClassifier(K=5)

# Train
mach = machine(model, X[train, :], y[train])
fit!(mach)

# Predict
y_pred = predict(mach, X[test, :])
accuracy = mean(y_pred .== y[test])

Flux for Neural Networks

using Flux
using Flux: onehotbatch, logitcrossentropy

# Define model
model = Chain(
    Dense(784, 256, relu),
    Dense(256, 128, relu),
    Dense(128, 10),
    softmax
)

# Loss function
loss(x, y) = logitcrossentropy(model(x), y)

# Training
opt = ADAM(0.001)
data = [(X_train, y_train)]

# Train loop
for epoch in 1:10
    Flux.train!(loss, params(model), data, opt)
    println("Epoch $epoch done")
end

Model Evaluation

using MLJ

# Cross-validation
cv = CV(nfolds=5)
evaluate(model, X, y, resampling=cv, measure=[rms, rmslp1])

# Hyperparameter tuning
ranges = range(model, :K, lower=1, upper=20)
tuning = Grid(resolution=5)
self_tuning_model = TunedModel(
    model=model,
    tuning=tuning,
    resampling=cv,
    ranges=ranges,
    measure=rms
)

# Fit tuned model
mach = machine(self_tuning_model, X, y)
fit!(mach)

Deep Learning Tips

# GPU support
using CUDA
model = model |> gpu  # move to GPU
X_gpu = X |> gpu

# Custom training
function train_step!(model, opt, x, y)
    grad = gradient(params(model)) do
        loss(model(x), y)
    end
    Flux.update!(opt, params(model), grad)
end

Common Mistakes

1. Not scaling features

ML models need scaled features. Use Standardizer from MLJ: mach = machine(Standardizer(), X); fit!(mach); Xs = transform(mach, X).

2. Data leakage

Scale after splitting, not before. Use MLJ's machine(model, X, y) which handles pipeline integrity.

3. Overfitting

Use cross-validation, regularization, and simpler models. MLJ's evaluation functions include train/test separation.

Practice Questions

1. How do you load a model in MLJ? @load KNNClassifier or @load RidgeRegressor pkg=MultivariateStats.

2. How do you train a neural network in Flux? Define the model with Chain, loss function, data loader, and Flux.train! loop.

3. How do you cross-validate in MLJ? evaluate(model, X, y, resampling=CV(nfolds=5), measure=[rms]).

FAQ

{{< faq question="Is Flux good for production?" >}} Flux is great for research and production. It's fast, GPU-compatible, and compiles to efficient code. For serving, convert to ONNX or use Flux's inference API. {{< /faq >}}

{{< faq question="How do I save trained models?" >}} using BSON; BSON.@save "model.bson" model for Flux. MLJ has save(mach, "model.jlso"). {{< /faq >}}

{{< faq question="What GPU support does Flux have?" >}} CUDA.jl for NVIDIA GPUs, AMDGPU.jl for AMD. Most operations automatically move to GPU with |> gpu. {{< /faq >}}

What's Next

Now learn about statistics in Julia.