Supervised Learning in Machine Learning

Machine Learning

Supervised Learning

• Definition: A type of machine learning where the algorithm is trained on labeled data to learn the relationship between input data and output labels.
• Goal: The algorithm learns to predict the output label for new, unseen input data.
• Types of problems:
  • Regression: Predicting a continuous output value (e.g. predicting house prices).
  • Classification: Predicting a categorical output label (e.g. spam vs. not spam emails).
• Training process:
  1. Collect and label a dataset.
  2. Train the algorithm on the labeled data.
  3. Test the algorithm on a separate dataset to evaluate its performance.
• Examples of algorithms:
  • Linear Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVMs)

Unsupervised Learning

• Definition: A type of machine learning where the algorithm is trained on unlabeled data to discover patterns or relationships.
• Goal: The algorithm learns to identify clusters, dimensions, or anomalies in the data.
• Types of problems:
  • Clustering: Grouping similar data points into clusters.
  • Dimensionality reduction: Reducing the number of features in the data while retaining important information.
  • Anomaly detection: Identifying data points that are significantly different from the rest.
• Training process:
  1. Collect a dataset.
  2. Train the algorithm on the dataset to identify patterns or relationships.
  3. Evaluate the algorithm's performance using metrics such as clustering quality or anomaly detection accuracy.
• Examples of algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

Machine Learning

Supervised Learning

• Supervised learning involves training an algorithm on labeled data to learn the relationship between input data and output labels.
• The goal is to predict the output label for new, unseen input data.
• There are two types of supervised learning problems:
  • Regression problems, which involve predicting a continuous output value.
  • Classification problems, which involve predicting a categorical output label.
• The supervised learning process involves:
  • Collecting and labeling a dataset.
  • Training the algorithm on the labeled data.
  • Testing the algorithm on a separate dataset to evaluate its performance.
• Examples of supervised learning algorithms include:
  • Linear Regression.
  • Decision Trees.
  • Random Forest.
  • Support Vector Machines (SVMs).

Unsupervised Learning

• Unsupervised learning involves training an algorithm on unlabeled data to discover patterns or relationships.
• The goal is to identify clusters, dimensions, or anomalies in the data.
• There are three types of unsupervised learning problems:
  • Clustering problems, which involve grouping similar data points into clusters.
  • Dimensionality reduction problems, which involve reducing the number of features in the data while retaining important information.
  • Anomaly detection problems, which involve identifying data points that are significantly different from the rest.
• The unsupervised learning process involves:
  • Collecting a dataset.
  • Training the algorithm on the dataset to identify patterns or relationships.
  • Evaluating the algorithm's performance using metrics such as clustering quality or anomaly detection accuracy.
• Examples of unsupervised learning algorithms include:
  • K-Means Clustering.
  • Hierarchical Clustering.
  • Principal Component Analysis (PCA).
  • t-Distributed Stochastic Neighbor Embedding (t-SNE).