AI Foundations

Artificial Intelligence: Foundations and Real-World Applications Artificial intelligence helps machines learn from data to perform tasks that usually require human thinking. It rests on three main pieces: data, algorithms, and computing power. A model learns from many examples and then makes predictions on new inputs. The aim is to build tools that support people, improve decisions, and save time. Foundations Key ideas include data quality, representation, and how we train and measure success. Good data helps models work well beyond the training set. ...

Artificial Intelligence Foundations for Practitioners Foundations in AI help teams turn ideas into reliable products. They connect data quality, model choice, evaluation, and governance. With a solid base, practitioners can move from experiments to real-world impact while keeping risk under control. Core ideas Data quality and scope: Define the task, gather representative samples, check for bias, missing values, and labeling errors; document data sources. Model selection and bias: Match the task to a model type and its assumptions; simple baselines can beat fancy tricks. Be aware of bias in data and predictions. Evaluation and reliability: Use suitable metrics, proper validation, and calibration to understand both accuracy and reliability across groups. Governance and transparency: Record decisions, privacy controls, and explainability plans; share results with stakeholders. Practical workflow An effective workflow follows a clear objective, careful data prep, testing, deployment, and ongoing monitoring. At each stage, set expectations and guardrails. ...

Artificial intelligence foundations for developers Building AI features starts with a clear problem and honest constraints. Developers benefit from a simple map: what to know, what to measure, and how to ship safely. This article covers fundamental ideas that help you create reliable AI-powered apps. Core concepts Training vs inference: training tunes a model once; inference runs it to answer many requests. Data quality: good data improves results; biased or noisy data hurts outcomes. Evaluation: pick metrics that reflect user value, not only raw accuracy. Latency and cost: response time and compute price affect the user experience. Transfer learning: reuse existing models to save time and improve results. Data matters Data drives AI behavior. Use clean, representative data and protect user privacy. Minimize data collection, label thoughtfully, and document data sources. If data shifts, you may need to adjust prompts, fine-tune, or update the model version. ...