Types of Machine Learning with Code Examples

Types of Machine Learning with Code Examples

There are 4 types of Machine Learning:

  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning

Let's see them one by one in detail.

1. Supervised Learning

Definition: Supervised learning is a type of machine learning where the algorithm learns from labeled training data.

Explanation:

  • In supervised learning, the algorithm learns from labeled data, where each training example has an associated output label.
  • The goal is to learn a mapping from inputs to outputs.

Applications:

  • Predicting house prices based on features like size, location, and number of bedrooms.
  • Classifying emails as spam or not spam based on their content.
  • Detecting fraudulent transactions in financial data.

Code Example (Python - Scikit-Learn):


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train a K-Nearest Neighbors classifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# Make predictions on the test data
y_pred = knn.predict(X_test)

# Evaluate the model
accuracy = knn.score(X_test, y_test)
print(f'Accuracy: {accuracy}')

2. Unsupervised Learning

Definition: Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data.

Explanation:

  • In unsupervised learning, the algorithm learns from data without explicit output labels.
  • The goal is to find hidden patterns or structures in the input data.

Applications:

  • Customer segmentation for targeted marketing.
  • Grouping news articles into topics for recommendation systems.
  • Anomaly detection to identify unusual behavior in data.

Code Example (Python - Scikit-Learn):


from sklearn.datasets import load_iris
from sklearn.cluster import KMeans

# Load the Iris dataset
iris = load_iris()
X = iris.data

# Initialize and fit a K-Means clustering model
kmeans = KMeans(n_clusters=3, random_state=42)
kmeans.fit(X)

# Get the cluster labels
labels = kmeans.labels_

print(labels)

3. Semi-Supervised Learning

Definition: Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data.

Explanation:

  • Semi-supervised learning leverages a small amount of labeled data along with a larger amount of unlabeled data.
  • It combines the benefits of labeled data for learning structure and unlabeled data for generalization.

Applications:

  • Sentiment analysis in text data where only a small subset of data is labeled.
  • Medical diagnosis using a combination of labeled patient data and unlabeled medical records.
  • Image classification with a limited number of labeled images and a large collection of unlabeled images.

Code Example (Python - Scikit-Learn):


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the data into a small labeled set and a larger unlabeled set
X_labeled, X_unlabeled, y_labeled, _ = train_test_split(X, y, test_size=0.8, random_state=42)

# Initialize and train a Support Vector Machine classifier
svm = SVC(kernel='linear')
svm.fit(X_labeled, y_labeled)

# Make predictions on new data
predictions = svm.predict(X_unlabeled)

print(predictions)

4. Reinforcement Learning

Definition: Reinforcement learning is a type of machine learning where an agent learns to make decisions by trial and error.

Explanation:

  • Reinforcement learning involves an agent interacting with an environment and learning from feedback in the form of rewards or penalties.
  • The goal is to learn a policy that maximizes cumulative rewards over time.

Applications:

  • Game playing, such as chess or Go, where the agent learns to make strategic moves.
  • Robotics, where the agent learns to perform tasks like navigating an environment or manipulating objects.
  • Autonomous vehicles, where the agent learns to drive safely and efficiently.

Code Example (Python - OpenAI Gym):


import gym
import numpy as np

# Create the CartPole environment
env = gym.make('CartPole-v1')

# Initialize the Q-table
Q = np.zeros((env.observation_space.n, env.action_space.n))

# Define hyperparameters
alpha = 0.1  # Learning rate
gamma = 0.6  # Discount factor
epsilon = 0.1  # Exploration rate

# Training loop
for episode in range(1, 1001):
    state = env.reset()
    done = False

    while not done:
        if np.random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()  # Explore action space
        else:
            action = np.argmax(Q[state])  # Exploit learned values

        next_state, reward, done, _ = env.step(action)

        # Update Q-value using Q-learning update rule
        Q[state][action] += alpha * (reward + gamma * np.max(Q[next_state]) - Q[state][action])
        state = next_state

# Evaluate the trained agent
total_rewards = 0
test_episodes = 100

for _ in range(test_episodes):
    state = env.reset()
    done = False

    while not done:
        action = np.argmax(Q[state])
        state, reward, done, _ = env.step(action)
        total_rewards += reward

average_reward = total_rewards / test_episodes
print(f'Average reward: {average_reward}')

Explanation

This code implements the Q-learning algorithm to train an agent to balance a pole on a moving cart in the CartPole environment.

  • We create the CartPole environment using OpenAI Gym.
  • We initialize a Q-table to store Q-values for state-action pairs.
  • We define hyperparameters such as learning rate (alpha), discount factor (gamma), and exploration rate (epsilon).
  • The training loop iterates through episodes, where the agent interacts with the environment, updates Q-values using the Q-learning update rule, and learns a policy.
  • The trained agent is then evaluated over test episodes to calculate the average reward obtained.
Location: Pune, Maharashtra, India

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