Certainly! Keep in mind that machine learning is a vast subject, but to get started I can provide a simple python decision tree example that can be helpful to understand subject when you are just starting out: 

``` python
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.datasets import load_iris

# Load flower iris dataset to use as an example
data = load_iris()

# Split training and testing inputs (X) and labels (y) 
X_train = train[['sepal_length','sepal_width','petal_length','petal_width']]
y_train = train.species
X_test = test[['sepal_length','sepal_width','petal_length','petal_width']]
y_test = test.species

# Train a decision tree classifier
mod_dt = DecisionTreeClassifier(max_depth = 3, random_state = 1)
mod_dt.fit(X_train,y_train)

# Visualize the fitted decision tree
plt.figure(figsize = (10,8))
fn = ["sepal_length", "sepal_width", "petal_length", "petal_width"]
cn = ['setosa', 'versicolor', 'virginica']
plot_tree(mod_dt, feature_names = fn, class_names = cn, filled = True)

# Get predictions for the test set
prediction=mod_dt.predict(X_test)
```

You will need a python kernel to run this code and you'll need to have the packages sklearn and matplotlib installed. You can modify the DecisionTreeClassifier parameters max_depth and random_state to see how the fitted tree will change. Is there something that you would like me to elaborate on, or did you have something else in mind?
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