What is Machine Learning?

Machine learning (ML) is a type of artificial intelligence (AI) that allows computer systems to learn from data without explicit programming. It enables computers to identify patterns, make predictions, and improve their performance over time.

Machine learning is used in a wide range of applications, including:

• Image recognition
• Natural language processing
• Fraud detection
• Recommendation systems
• Self-driving cars

Scikit-Learn: A Powerful Machine Learning Library

Scikit-learn is a popular Python library that provides a wide range of machine learning algorithms and tools. It is easy to use, well-documented, and widely used in the industry.

Scikit-learn offers algorithms for:

• Supervised learning
• Unsupervised learning
• Model selection
• Preprocessing

Supervised Learning with Scikit-Learn

Supervised learning is a type of machine learning where the algorithm learns from labeled data. This means that each data point has a corresponding output or label. The goal of supervised learning is to learn a function that maps from the input features to the output label.

Example: Linear Regression

Linear regression is a simple yet powerful supervised learning algorithm used for predicting a continuous output variable based on one or more input features.

      
        from sklearn.linear_model import LinearRegression
        from sklearn.model_selection import train_test_split

        # Load your dataset (replace with your own data)
        X = ...  # Input features
        y = ...  # Output variable

        # 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)

        # Create a linear regression model
        model = LinearRegression()

        # Train the model on the training data
        model.fit(X_train, y_train)

        # Make predictions on the testing data
        y_pred = model.predict(X_test)

        # Evaluate the model performance
        # ...
      
    

Unsupervised Learning with Scikit-Learn

Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. In this case, there are no corresponding outputs or labels provided. The goal of unsupervised learning is to discover patterns and insights in the data.

Example: K-Means Clustering

K-means clustering is a popular unsupervised learning algorithm used for grouping data points into clusters based on their similarity.

      
        from sklearn.cluster import KMeans

        # Load your dataset (replace with your own data)
        X = ...  # Input features

        # Create a KMeans model with desired number of clusters
        kmeans = KMeans(n_clusters=3)

        # Fit the model to the data
        kmeans.fit(X)

        # Get the cluster labels for each data point
        labels = kmeans.labels_

        # ...