1. Линейная регрессия:
   Пример кода (Python – scikit-learn):

   from sklearn.linear_model import LinearRegression
   # Create a linear regression model
   model = LinearRegression()
   # Fit the model to the training data
   model.fit(X_train, y_train)
   # Make predictions on the test data
   y_pred = model.predict(X_test)

2. Логистическая регрессия:
   Пример кода (Python – scikit-learn):

   from sklearn.linear_model import LogisticRegression
   # Create a logistic regression model
   model = LogisticRegression()
   # Fit the model to the training data
   model.fit(X_train, y_train)
   # Make predictions on the test data
   y_pred = model.predict(X_test)

3. Деревья решений:
   Пример кода (Python – scikit-learn):

   from sklearn.tree import DecisionTreeClassifier
   # Create a decision tree model
   model = DecisionTreeClassifier()
   # Fit the model to the training data
   model.fit(X_train, y_train)
   # Make predictions on the test data
   y_pred = model.predict(X_test)

4. Случайные леса:
   Пример кода (Python – scikit-learn):

   from sklearn.ensemble import RandomForestClassifier
   # Create a random forest model
   model = RandomForestClassifier()
   # Fit the model to the training data
   model.fit(X_train, y_train)
   # Make predictions on the test data
   y_pred = model.predict(X_test)

5. Машины опорных векторов (SVM):
   Пример кода (Python – scikit-learn):

   from sklearn.svm import SVC
   # Create a support vector machine model
   model = SVC()
   # Fit the model to the training data
   model.fit(X_train, y_train)
   # Make predictions on the test data
   y_pred = model.predict(X_test)

6. Нейронные сети:
   Пример кода (Python – TensorFlow):

   import tensorflow as tf
   # Create a neural network model
   model = tf.keras.models.Sequential([
      tf.keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),
      tf.keras.layers.Dense(64, activation='relu'),
      tf.keras.layers.Dense(num_classes, activation='softmax')
   ])
   # Compile the model
   model.compile(optimizer='adam',
                loss='categorical_crossentropy',
                metrics=['accuracy'])
   # Train the model
   model.fit(X_train, y_train, epochs=10, batch_size=32)
   # Evaluate the model
   loss, accuracy = model.evaluate(X_test, y_test)

Это всего лишь несколько примеров методов машинного обучения. Существует множество других алгоритмов и методов, каждый из которых имеет свои преимущества и варианты использования.