Machine Learning Algorithms

Machine learning algorithms are the backbone of modern data science and artificial intelligence. These algorithms enable systems to learn from data, make predictions, and improve over time without being explicitly programmed. Below are some of the most common machine learning algorithms:

Supervised Learning Algorithms

• Linear Regression: This algorithm models the relationship between a dependent variable and one or more independent variables.

  • 

    Linear Regression

• Logistic Regression: It is used for binary classification problems, predicting the probability of an event.

  • 

    Logistic Regression

• Decision Trees: These are tree-like models that predict a class or value based on a set of features.

  • 

    Decision Tree

• Random Forest: This is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

  • 

    Random Forest

Unsupervised Learning Algorithms

• K-Means Clustering: This algorithm partitions the dataset into K distinct, non-overlapping subgroups (clusters).

  • 

    K-Means Clustering

• Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that is often used to reduce the dimensionality of large datasets, increasing interpretability while minimizing information loss.

  • 

    PCA

• Association Rules: These algorithms are used to discover interesting relations between variables in large databases.

  • 

    Association Rules

For more in-depth information on machine learning algorithms, you can explore our Machine Learning Tutorial.