Machine learning (ML) is the subset of artificial intelligence (AI) focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions or predictions without explicit, hard-coded instructions. Machine learning, and in particular deep learning, is the backbone of most modern AI systems.
In this comprehensive guide, you will find a collection of machine learning-related content such as educational explainers, hands-on tutorials, podcast episodes and much more.
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As the first step in your journey, explore introductory machine learning explainers to obtain a high-level understanding.
Explore the fundamental data science and statistical principles that power machine learning use cases.
Feature engineering is the process of selecting, transforming and creating new features from raw data to improve the performance of ML models.
Supervised learning uses human-labeled input and output datasets to train ML models.
Unsupervised learning analyzes and clusters unlabeled datasets by discovering hidden patterns or data groupings without the need for human input.
Semi-supervised learning combines supervised and unsupervised learning by using both labeled and unlabeled data to train models for classification and regression tasks.
Reinforcement learning allows an autonomous agent to learn through trial and error, receiving feedback in the form of rewards or penalties for its actions.
Deep learning uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.
Generative AI can create original content such as text, images, video, audio or software code in response to a user’s prompt or request.
Model training is the process of “teaching” a machine learning model to optimize performance on a training dataset of sample tasks relevant to the model’s eventual use cases.
Machine learning libraries are collections of pre-written code, functions and tools that simplify the development and implementation of ML algorithms and models.
MLOps, short for machine learning operations, is a set of practices designed to help practitioners create standardized processes for building and running ML models.
Natural language processing (NLP) allows a model to process human language through computational linguistics and statistical techniques.
Computer vision uses ML to teach computers and systems to “see,” that is to derive meaningful information from digital images, videos and other visual inputs.