Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning, each with distinct methodologies for learning from data or experience. Links Notes and resources at ocdevel.com/mlg/4 Try a walking desk stay healthy & sharp while you learn & code The Role of Machine Learning in Artificial Intelligence Artificial intelligence includes subfields such as reasoning, knowledge representation, search, planning, and learning. Learning connects to other AI subfields by enabling systems to improve from mistakes and past actions. The Core Machine Learning Process The machine learning process follows three steps: prediction (or inference), error evaluation (or loss calculation), and training (or learning). In an example such as predicting chess moves, a move is made (prediction), the error or effectiveness of that move is measured (error function), and the underlying model is updated based on that error (learning). This process generalizes to real-world applications like predicting house prices, where a model is trained on a large dataset with many features. Data, Features, and Models Datasets used for machine learning are typically structured as spreadsheets with rows as examples (e.g., individual houses) and columns as features (e.g., number of bedrooms, bathrooms, square footage). Features are variables used by algorithms to make predictions and can be numerical (such as square footage) or categorical (such as "is downtown" yes/no). The algorithm processes input data, learns the appropriate coefficients or weights for each feature through algebraic equations, and forms a model. The combination of the algorithm (such as code in Python or TensorFlow) and the learned weights forms the model, which is then used to make future predictions. Online Learning and Model Updates After the initial training on a dataset, models can be updated incrementally with new data (called online learning). When new outcomes are observed that differ from predictions, this new information is used to further train and improve the model. Categories of Machine Learning Algorithms Machine learning algorithms are broadly grouped into three categories: supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data, where the model is trained with known inputs and outputs, such as predicting prices (continuous values) or classes (like cat/dog/tree). Unsupervised learning finds similarities within data without labeled outcomes, often used for clustering or segmentation tasks such as organizing users for advertising. Reinforcement learning involves an agent taking actions in an environment to achieve a goal, receiving rewards or penalties, and learning the best strategies (policies) over time. Examples and Mathematical Foundations Regression algorithms like linear regression are commonly used supervised learning techniques to predict numeric outcomes. The process is rooted in algebra and particularly linear algebra, where matrices represent datasets and the algorithm solves for optimal coefficient values. The model's equation generated during training is used for making future predictions, and errors from predictions guide further learning. Recommended Resources MachineLearningMastery.com: Accessible articles on ML basics. Podcast's own curated learning paths: ocdevel.com/mlg/resources. The book "The Master Algorithm" offers an introductory and audio format overview of foundational machine learning algorithms and concepts.
