Machine learning and artificial intelligence

Chapter 3: Unsupervised Learning in Machine Learning Unsupervised learning is a fundamental branch of machine learning where the model is trained on data without explicit labels. Unlike supervised learning, which relies on input-output pairs for training, unsupervised learning algorithms must discover patterns and relationships in the data autonomously. This chapter delves into the core concepts, mathematical foundations, and practical applications of unsupervised learning, accompanied by Python code examples to solidify the understanding. 1. Introduction to Unsupervised Learning Unsupervised learning involves training a model to identify underlying patterns in a dataset without predefined labels. The most common tasks under this paradigm include clustering, dimensionality reduction, and anomaly detection. 1.1. Clustering Clustering involves grouping a set of objects such that objects in the same group (or cluster) are more similar to each other than to those in other clusters. Common ...

Machine Learning (Chapter 2): Supervised Learning Machine Learning (ML) has transformed numerous industries by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. Among the core branches of ML is Supervised Learning , which is crucial for applications ranging from predicting house prices to diagnosing diseases. This chapter explores the principles, methodologies, and mathematics behind Supervised Learning, complemented by Python code to illustrate key concepts. What is Supervised Learning? Supervised Learning is a type of machine learning where an algorithm learns from a labeled dataset. In this context, "labeled" means that each training example is paired with an output label. The goal is to learn a mapping from inputs to outputs, which can then be used to predict the output for new, unseen data. Example Imagine teaching a child to recognize different animals. You show them a picture of a cat and say, "This is a c...

Chapter 1: Introduction to Machine Learning What is Machine Learning? Machine Learning (ML) is a branch of artificial intelligence (AI) that empowers computers to learn from data without being explicitly programmed. Unlike traditional programming, where a developer writes a specific set of instructions to solve a problem, machine learning enables a system to identify patterns, make decisions, and improve its performance over time through experience. The term "machine learning" was first coined by Arthur Samuel in 1959, who defined it as a "field of study that gives computers the ability to learn without being explicitly programmed." Since then, the field has evolved, becoming a cornerstone of modern technology, influencing everything from recommendation systems on streaming platforms to autonomous vehicles. Why Machine Learning Matters In the era of big data, machine learning has become indispensable. The vast amounts of data generated daily by businesses, governmen...