day-26.jl

# Day 26: Working with machine learning and artificial intelligence

# Learn how to work with machine learning and artificial intelligence in Julia, including how to train and use models for prediction, classification, and clustering
# Practice using built-in and external libraries to implement machine learning and AI algorithms in your programs
# Example code:

# Import the Flux package and use it to train a simple neural network
using Flux
model = Chain(
    Dense(10, 5, σ),
    Dense(5, 2),
    softmax
)
loss(x, y) = Flux.crossentropy(model(x), y)
optimizer = ADAM()
data = [(rand(10), rand(2)) for i = 1:1000]
Flux.train!(loss, params(model), data, optimizer)

# Use the trained model to make predictions on new data
x = rand(10)
y = model(x)
println(y)

# Import the Clustering package and use it to cluster a set of data points
using Clustering
data = [rand(2) for i = 1:100]
kmeans = Kmeans(3)
clusters = fit(kmeans, data)
println(clusters.assignments)

# Import the DecisionTree package and use it to train a decision tree
using DecisionTree
features = [:height, :weight, :hair_color, :eye_color]
labels = [:male, :female]
data = [
    (1.8, 75, "black", "brown"),
    (1.7, 60, "brown", "blue"),
    (1.6, 45, "blonde", "green"),
    # ...
]
model = DecisionTree.DecisionTreeClassifier(max_depth=2)
DecisionTree.fit!(model, features, labels, data)

# Use the trained model to make predictions on new data
x = (1.9, 80, "black", "brown")
y = DecisionTree.predict(model, x)
println(y)