Machine learning covers a broad range of techniques to solve problems—from predicting continuous values to classifying images. In this post, I’ll break down the core ideas behind some of the most important machine learning algorithms. Each section provides a clear explanation along with a link to a trusted source (mostly Wikipedia) for further details.
1. Supervised Learning Algorithms
Supervised learning algorithms learn a mapping from input data (features) to known outputs (labels). They are typically used in regression and classification tasks.
Linear Regression
Linear regression is used to predict a continuous outcome from one or more input features. The model fits a straight line (or hyperplane in higher dimensions) through the data by minimizing the differences between predicted and actual values. It’s one of the simplest yet most powerful techniques for understanding relationships in data.
Learn more about Linear Regression
Logistic Regression
Logistic regression is similar in spirit to linear regression but is used for binary classification problems. Instead of predicting a continuous value, it predicts the probability of an observation belonging to one of two classes by using a logistic (sigmoid) function. The outcome is typically interpreted as “yes” or “no” based on a threshold (often 0.5).
Learn more about Logistic Regression
k-Nearest Neighbors (KNN)
The k-nearest neighbors algorithm is a simple, instance-based learning method. It classifies a new data point based on the majority label of its k closest neighbors in the feature space. It works well when the decision boundary is irregular and doesn’t require an explicit training phase—just a good distance metric.
Learn more about k-Nearest Neighbors
Decision Trees
Decision trees split the data into branches based on feature values, forming a tree-like structure. At each node, a decision rule is applied to partition the data until a prediction is made at a leaf node. They are intuitive and easy to interpret, making them popular for both classification and regression tasks.
Learn more about Decision Trees
Random Forests
Random forests are an ensemble method that builds many decision trees on different subsets of the data and then combines their results (usually by voting in classification or averaging in regression). This process reduces overfitting and improves generalization, offering more robust predictions than a single decision tree.
Learn more about Random Forests
Support Vector Machines (SVM)
Support vector machines work by finding the optimal hyperplane that separates different classes in the feature space. The goal is to maximize the margin—the distance between the hyperplane and the nearest data points from each class. SVMs are especially effective in high-dimensional spaces.
Learn more about Support Vector Machines
Naive Bayes
Naive Bayes is a probabilistic classifier that applies Bayes’ theorem with the “naïve” assumption that all features are independent given the class label. Despite this simplification, it performs surprisingly well for many real-world problems such as spam filtering and document classification.
Learn more about Naive Bayes
Neural Networks
Neural networks are inspired by the human brain. They consist of layers of interconnected nodes (neurons) that learn to represent complex patterns in the data. With the rise of deep learning, neural networks have become the cornerstone for applications like image recognition, natural language processing, and more.
Learn more about Artificial Neural Networks
Ensemble Methods – Boosting
Boosting is an ensemble technique that sequentially trains weak learners—often decision trees—where each new model focuses on correcting the errors made by the previous ones. The final model is a weighted combination of these weak learners, resulting in a strong overall predictor. Common boosting algorithms include AdaBoost and Gradient Boosting.
Learn more about AdaBoost
Learn more about Gradient Boosting
2. Unsupervised Learning
Unsupervised learning algorithms discover hidden structures in data without the use of labeled outcomes. They are often used for clustering or reducing the number of features in high-dimensional datasets.
k-Means Clustering
k-Means clustering partitions the data into k clusters by assigning each point to the nearest cluster center (centroid) and then updating these centers iteratively. The algorithm minimizes the variance within each cluster, effectively grouping similar data points together.
Learn more about k-Means Clustering
Principal Component Analysis (PCA)
Principal component analysis (PCA) is a dimensionality reduction technique that transforms the original features into a new set of uncorrelated variables (principal components). These components are ordered so that the first few retain most of the variation present in the original data. This is particularly useful for visualizing high-dimensional data or speeding up downstream algorithms.
Learn more about PCA
Final Thoughts
Each of these algorithms has its unique strengths and is suited for different types of problems. Whether you’re working on a regression task, a classification challenge, or uncovering hidden patterns in your data, understanding these methods will help you choose the right tool for your project.
I hope this clear and friendly overview helps demystify these important machine learning algorithms.