{"id":224,"date":"2017-12-18T02:07:00","date_gmt":"2017-12-18T02:07:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2017\/12\/18\/introduction-to-machine-learning-ml\/"},"modified":"2026-08-16T12:14:06","modified_gmt":"2026-08-16T10:14:06","slug":"introduction-to-machine-learning-ml","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/introduction-to-machine-learning-ml\/","title":{"rendered":"Introduction to Machine Learning (ML)"},"content":{"rendered":"<p><!-- ktg-updated-banner --><\/p>\n<div class=\"ktg-updated-banner\" style=\"margin:1em 0;padding:0.75em 1em;background:#eff6ff;border-left:4px solid #3b82f6;border-radius:4px;\">\n<p><strong>Updated August 16, 2026:<\/strong> Clarified the main types of machine learning (including reinforcement learning), fixed heading structure for SEO, added a table of contents and FAQ, and linked related beginner tutorials on <a href=\"https:\/\/kindsonthegenius.com\/blog\/category\/machine-learning\/\">The Genius Blog<\/a>.<\/p>\n<\/div>\n<p><strong>Introduction to machine learning<\/strong> starts with a simple idea: systems that improve at a task by learning patterns from data, instead of being fully hand-coded for every case. Another useful definition: machine learning is the process of deducing <em>unknown<\/em> values from <em>known<\/em> values.<\/p>\n<p>This guide covers what machine learning is, a short theory example, and the main types of machine learning problems \u2014 supervised, unsupervised, and reinforcement learning \u2014 including classification vs regression.<\/p>\n<p><!-- ktg-article-toc --><\/p>\n<nav class=\"ktg-article-toc\" aria-label=\"Introduction to machine learning table of contents\" style=\"margin:1.5em 0;padding:1em 1.25em;background:#f8fafc;border:1px solid #e2e8f0;border-radius:8px;\">\n<h2>Table of Contents<\/h2>\n<ol>\n<li><a href=\"#what-is-machine-learning\">What is machine learning?<\/a><\/li>\n<li><a href=\"#theory-of-machine-learning\">The theory behind machine learning<\/a><\/li>\n<li><a href=\"#classification-example\">A simple classification example<\/a><\/li>\n<li><a href=\"#types-of-machine-learning\">Types of machine learning problems<\/a><\/li>\n<li><a href=\"#faq\">Frequently asked questions<\/a><\/li>\n<li><a href=\"#next-steps\">Next steps and further reading<\/a><\/li>\n<\/ol>\n<\/nav>\n<h2 id=\"what-is-machine-learning\">What Is Machine Learning?<\/h2>\n<p>Machine learning is a branch of artificial intelligence that gives a computer system the ability to progressively learn and improve performance on tasks without being explicitly programmed for every detail of those tasks.<\/p>\n<p>In practice, you collect examples (data), choose a model, and train it so that it can make useful predictions or discover structure on new inputs it has not seen before.<\/p>\n<figure style=\"margin:1.5em 0;text-align:center;\">\n<img fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/08\/introduction-to-machine-learning-types-1.png\" alt=\"Diagram of types of machine learning: supervised learning, unsupervised learning, and reinforcement learning\" width=\"1200\" height=\"630\" loading=\"eager\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">Figure 1: Main types of machine learning problems \u2014 supervised, unsupervised, and reinforcement learning<\/figcaption><\/figure>\n<h2 id=\"theory-of-machine-learning\">The Theory Behind Machine Learning<\/h2>\n<p>Remember the second definition: determine unknown values given a set of known values.<\/p>\n<p><strong>Example:<\/strong> Class 5 students usually score between 70% and 100%. Class 3 students usually score between 40% and 60%. A new student scores 45%. Which class would you place them in?<\/p>\n<p>Most people choose Class 3, because 45% falls in the Class 3 range. That intuition is classification: assign a new observation to a category using patterns learned from labeled examples.<\/p>\n<h2 id=\"classification-example\">A Simple Classification Example<\/h2>\n<p>Given a training dataset of <em>N<\/em> observations {x<sub>n<\/sub>} (n = 1, \u2026, N) and corresponding target values {t<sub>n<\/sub>}, the goal of machine learning is to predict <em>t<\/em> for a new value of <em>x<\/em>.<\/p>\n<p>The usual approach is to construct a function y(x). For a new input x, y(x) is the prediction for t. We also care about uncertainty \u2014 often written as p(t | x), the probability of t given x. That captures how confident the model is.<\/p>\n<p>That is the core theory in plain language: <strong>make predictions about what is not yet known<\/strong>, using structure learned from what is known. For a deeper walkthrough of classification, see <a href=\"https:\/\/kindsonthegenius.com\/blog\/classification-in-machine-learning\/\">Classification in Machine Learning<\/a>.<\/p>\n<h2 id=\"types-of-machine-learning\">Types of Machine Learning Problems<\/h2>\n<p>Machine learning problems are commonly grouped into <strong>supervised learning<\/strong>, <strong>unsupervised learning<\/strong>, and <strong>reinforcement learning<\/strong>. Related settings include semi-supervised learning and active learning.<\/p>\n<h3 id=\"supervised-learning\">Supervised Learning<\/h3>\n<p>In supervised learning, the training set includes input vectors {x} <em>and<\/em> corresponding target values. The task is to find a function f(x) such that f(x) \u2248 t for new inputs.<\/p>\n<p>Supervised problems are usually either <strong>classification<\/strong> or <strong>regression<\/strong>.<\/p>\n<p><strong>Classification:<\/strong> Targets come from a finite set of categories. The model assigns each input to one of those discrete classes (for example, spam vs not spam).<\/p>\n<p><strong>Regression:<\/strong> Targets are continuous values in real-valued space (for example, predicting a house price). For a practical regression walkthrough, see <a href=\"https:\/\/kindsonthegenius.com\/blog\/machine-learning-101-basics-of-logistic-regression\/\">Machine Learning 101 \u2014 Basics of Logistic Regression<\/a> and related regression tutorials on the blog.<\/p>\n<h3 id=\"unsupervised-learning\">Unsupervised Learning<\/h3>\n<p>Unsupervised learning looks for patterns or structure in the input data without target labels. Common goals include:<\/p>\n<ul>\n<li><strong>Clustering<\/strong> \u2014 group similar examples (see <a href=\"https:\/\/kindsonthegenius.com\/blog\/what-is-k-means-in-clustering-in-machine-learning\/\">What is K-Means in Clustering<\/a>)<\/li>\n<li><strong>Density estimation<\/strong> \u2014 model how data is distributed<\/li>\n<li><strong>Dimensionality reduction<\/strong> \u2014 map high-dimensional data to fewer dimensions (for example 2D\/3D) while keeping important structure<\/li>\n<\/ul>\n<h3 id=\"reinforcement-learning\">Reinforcement Learning<\/h3>\n<p>Reinforcement learning is a third major class of problem. An <em>agent<\/em> takes actions in an environment and receives rewards or penalties. Over time it learns a policy \u2014 which actions to take in which situations \u2014 to maximize expected reward.<\/p>\n<p>Classic applications include game playing and robotics. Reinforcement learning is <strong>not<\/strong> the same as supervised or unsupervised learning, and it is not a subtype of semi-supervised learning.<\/p>\n<h3 id=\"semi-supervised-active\">Semi-Supervised and Active Learning<\/h3>\n<p><strong>Semi-supervised learning<\/strong> uses a mix of labeled and unlabeled data when labels are expensive to obtain.<\/p>\n<p><strong>Active learning<\/strong> lets the model (or a training loop) choose which unlabeled examples should be labeled next, to improve performance with fewer labels.<\/p>\n<h2 id=\"faq\">Frequently Asked Questions<\/h2>\n<h3>What is machine learning in simple terms?<\/h3>\n<p>Machine learning is a field of AI where systems improve at a task by learning patterns from data instead of being fully hand-coded for every case.<\/p>\n<h3>What are the main types of machine learning?<\/h3>\n<p>The three core types beginners should know are supervised learning, unsupervised learning, and reinforcement learning. Semi-supervised and active learning are important related settings.<\/p>\n<h3>What is the difference between classification and regression?<\/h3>\n<p>Classification predicts discrete labels or categories. Regression predicts continuous numeric values.<\/p>\n<h3>Is reinforcement learning supervised or unsupervised?<\/h3>\n<p>Neither. Reinforcement learning learns from rewards and penalties through interaction with an environment, not from a fixed labeled dataset alone.<\/p>\n<p><!-- ktg-faq-schema --><br \/>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is machine learning in simple terms?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Machine learning is a field of AI where systems improve at a task by learning patterns from data instead of being fully hand-coded for every case.\"}},{\"@type\":\"Question\",\"name\":\"What are the main types of machine learning?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The three core types beginners should know are supervised learning, unsupervised learning, and reinforcement learning. Semi-supervised and active learning are important related settings.\"}},{\"@type\":\"Question\",\"name\":\"What is the difference between classification and regression?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Classification predicts discrete labels or categories. Regression predicts continuous numeric values.\"}},{\"@type\":\"Question\",\"name\":\"Is reinforcement learning supervised or unsupervised?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Neither. Reinforcement learning learns from rewards and penalties through interaction with an environment, not from a fixed labeled dataset alone.\"}}]}<\/script><\/p>\n<h2 id=\"next-steps\">Next Steps and Further Reading<\/h2>\n<p>You now have an overview of machine learning: definitions, the prediction framing, and the main problem types. Next, go deeper with these beginner tutorials:<\/p>\n<ul>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/basics-of-neural-networks-in-ai-artificial-intelligence\/\">Basics of Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/basics-of-perceptron-in-neural-networks\/\">Basics of Perceptron in Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/classification-in-machine-learning\/\">Classification in Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/what-is-k-means-in-clustering-in-machine-learning\/\">What is K-Means in Clustering<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/introduction-to-support-vector-machine-svm\/\">Introduction to Support Vector Machines (SVM)<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/category\/machine-learning\/\">All Machine Learning tutorials<\/a><\/li>\n<\/ul>\n<p><!-- ktg-alkademy-cta --><\/p>\n<p style=\"margin:1.5em 0;padding:1em 1.25em;background:#f8fafc;border:1px solid #e2e8f0;border-radius:8px;\"><strong>Want structured ML training?<\/strong> Join <a href=\"https:\/\/www.alkademy.com\/courses\" target=\"_blank\" rel=\"noopener noreferrer\">Alkademy<\/a> for instructor-led machine learning and data science classes with hands-on projects.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Updated August 16, 2026: Clarified the main types of machine learning (including reinforcement learning), fixed heading structure for SEO, added a table of contents and &hellip; <\/p>\n","protected":false},"author":1,"featured_media":2349,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pagelayer_contact_templates":[],"_pagelayer_content":"","footnotes":""},"categories":[16],"tags":[],"class_list":["post-224","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning"],"acf":[],"_links":{"self":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/224","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/comments?post=224"}],"version-history":[{"count":6,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/224\/revisions"}],"predecessor-version":[{"id":2351,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/224\/revisions\/2351"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media\/2349"}],"wp:attachment":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media?parent=224"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/categories?post=224"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/tags?post=224"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}