## about this category
AI and Data brings together the two fronts that define the current era of computing: applied artificial intelligence — LLMs, agents, automation and the impact of AI on work — and the analytical foundation beneath it — statistics, Python for data and the machine learning models used in production. It is the right category for anyone who wants to apply AI today or build the technical grounding to work with data.
Topics in this category:#Artificial-Intelligence#Data-and-Machine-Learning
## all articles
AI and Data
- AI and Data
RAG: make an LLM answer with your own data
RAG retrieves relevant passages from your documents and injects them into the prompt before the model answers. See the architecture and how to evaluate it.
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Henrico - AI and Data
MCP (Model Context Protocol): the complete guide
MCP standardizes how AI models reach tools and data. Understand the client-server architecture, when to use it and how to write your own server.
Read articleHenrico - AI and Data
AI agents: what they are and how they work
AI agents plan and execute tasks with tools, not just answer. See the architecture, the real limits and where to apply them without getting burned.
Read articleHenrico - AI and Data
AI for Customer Service: A Complete and Practical Guide
AI for customer service uses chatbots, voicebots, and data analytics to automate and personalize support. See types, advantages, and how to implement.
Read articleHenrico - AI and Data
AI for marketing: personalization and results at scale
AI in marketing automates campaigns, personalizes the journey at scale, and optimizes ads with data. Learn the benefits, tools, and how to get started.
Read articleHenrico - AI and Data
Claude Code: Anthropic''s AI agent in the terminal
Understand what Claude Code is, Anthropic''s coding agent in the terminal: it reads the repository, edits files, runs tests, and opens pull requests.
Read articleHenrico - AI and Data
AI for Healthcare: what it is and how it transforms care
AI for healthcare uses machine learning to analyze medical data, support diagnoses, accelerate drugs, and personalize treatments with greater precision.
Read articleHenrico - AI and Data
Automation with AI: how it works and where to apply it
Automation with AI combines RPA with machine learning, NLP, and computer vision to perform complex tasks, make decisions, and learn without human intervention.
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Computational Immunology: What It Is and How It Works
Computational immunology applies bioinformatics, mathematical models, and machine learning to study and predict the behavior of the immune system.
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Ana-carolina - AI and Data
Simpson's Paradox: when aggregated data deceives
Simpson's Paradox occurs when a trend seen in subgroups disappears or reverses when the data is aggregated. See why it happens and how to handle it in Python.
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Renata - AI and Data
NumPy: what it is and what the Python library is for
NumPy is the Python library for numerical computing that provides multidimensional arrays and fast vectorized operations for data and science.
Read articleHenrico - AI and Data
Euclidean Distance: What It Is, Formula, and Uses in Python
Euclidean distance is the straight-line measure between two points, obtained by the root of the sum of squared differences; it is the basis of KNN and K-Means.
Read articleHenrico - AI and Data
Machine Learning fundamentals: concepts and algorithms
Machine Learning is the AI field where algorithms learn patterns from data. See the algorithms, learning types, and evaluation metrics.
Read articleRenata - AI and Data
Linear Regression types: simple, multiple, Ridge, and Lasso
Meet the types of linear regression — simple, multiple, polynomial, Ridge, and Lasso — with equations, use cases, and criteria for choosing the right model.
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Artificial Intelligence in Medicine: Benefits and Risks
AI in medicine accelerates diagnoses and personalizes treatments: the FDA has already authorized more than 1,250 AI-enabled devices. See benefits and risks.
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Data Mining vs Machine Learning vs Deep Learning
Data mining discovers patterns in existing datasets; machine learning trains models that learn; deep learning uses deep neural networks.
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How to Create Machine Learning Projects: A 5-Step Guide
Machine learning projects require data preparation, suitable algorithms, iterative pipelines, scalability, and metrics like recall and F1-Score.
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Machine Learning vs Artificial Intelligence: the difference
Artificial Intelligence is the broad field that simulates human capabilities; Machine Learning is the subarea where algorithms learn from data.
Read articleRenata - AI and Data
What is Power BI and what it is for in data analysis
Power BI is Microsoft''s business intelligence suite that turns raw data into interactive dashboards and reports for data-driven decisions.
Read articleRenata - AI and Data
Data Pipeline: what it is and how it works
A data pipeline is a series of automated processes that collect, transform and move data from source to destination for analysis and decision making.
Read articleHenrico - AI and Data
Georeferencing: what it is and how it works
Georeferencing is the process of assigning geographic coordinates to data or objects, allowing them to be located and analyzed precisely on a map.
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AI Tools for Design: 6 Image Generators
Midjourney, DALL-E, Leonardo AI, Stockimg, Bing Image Creator, and BlueWillow: compare six AI tools that generate images from text.
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Data Science: 65 Application Areas with Real Examples
Data science uses statistics and machine learning to drive decisions: see 65 application areas, from finance and health to smart cities.
Read articleRenata - AI and Data
How to study Machine Learning: methods and resources
To study Machine Learning, start with Python, statistics, and linear algebra, move on to algorithms, and practice on real projects on Kaggle.
Read articleHenrico & Renata - AI and Data
Linear Regression: What It Is, How to Model and Evaluate
Understand linear regression: how it models relationships between variables with a line, predicts continuous values, and is measured with R2 and RMSE.
Read articleHenrico - AI and Data
Artificial Intelligence Applications: Examples and Real Uses
AI is already applied in medicine, digital security, industry, routing, customer service, and marketing. See real examples, risks, and where to start.
Read articleHenrico - AI and Data
Machine Learning: what it is, how it works, and its uses
Machine Learning is the field of AI in which algorithms learn patterns from data to predict, classify and decide without explicit programming.
Read articleHenrico