by
Menu
LESSON
listen to the answer
ANSWER
Exploring and experimenting with AI requires access to data. Fortunately, there are numerous sources of open data available for various interests and projects. Here are some platforms and datasets that you can use to play with AI, ranging from beginner-friendly to more complex applications:
General Data Repositories:
Kaggle (https://www.kaggle.com/datasets): A platform for data science competitions that also provides a wide range of datasets on topics from economics to natural language processing, along with kernels (code snippets) for data analysis and modeling.
UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php): A collection of databases, domain theories, and data generators widely used by the machine learning community.
Google Dataset Search (https://datasetsearch.research.google.com/): A tool that enables the search for datasets stored across the web.
Government and Public Data:
Data.gov (https://www.data.gov/): The US government’s open data portal includes a wealth of data on topics like agriculture, education, and public safety.
EU Open Data Portal (https://data.europa.eu/en): Offers data from the European Union institutions and bodies on various topics.
UN Data (http://data.un.org/): A portal providing access to datasets from the United Nations on international development, education, energy, and more.
Social Sciences and Economics:
World Bank Open Data (https://data.worldbank.org/): Free and open access to global development data, including indicators on economy, environment, and social outcomes.
Pew Research Center (https://www.pewresearch.org/download-datasets/): Offers datasets used in their social, demographic, and economic research.
Health and Science:
NIH Clinical Trials Database (https://clinicaltrials.gov/): Provides access to information on publicly and privately supported clinical studies.
GenBank (https://www.ncbi.nlm.nih.gov/genbank/): An annotated collection of all publicly available DNA sequences.
Images and Natural Language:
ImageNet (http://www.image-net.org/): A large visual database designed for use in visual object recognition software research.
Common Crawl (https://commoncrawl.org/): An open repository of web crawl data that can be used for large-scale data processing projects.
Specialized AI Datasets:
OpenAI Datasets (https://openai.com/datasets/): OpenAI provides access to datasets used in their research, which cover a variety of advanced AI topics.
Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/): A collection of large network datasets from dozens of social, peer-to-peer, and web graphs.
When using these datasets, it’s important to respect license agreements and use the data ethically, especially when dealing with personal or sensitive information. These resources can provide a solid foundation for learning, experimenting, and building projects with AI across a wide range of domains.
Quiz
Analogy
Imagine you’re a chef eager to master cuisines from around the world (exploring AI). The ingredients you need (data) are spread across various markets (data repositories) each specializing in different regional ingredients.
Kaggle is like a bustling international food market where you can find exotic spices (diverse datasets) and recipes (kernels) shared by other chefs. It’s a place to participate in cooking contests (competitions) and learn new culinary techniques.
UCI Machine Learning Repository resembles a well-established specialty store, known for its high-quality, classic ingredients (datasets) that have been used by chefs (researchers) for years to craft traditional dishes (machine learning models).
Google Dataset Search is akin to a magical cookbook that directs you to the exact location of the rarest ingredients (datasets) hidden in stores across the city (the web), making your ingredient hunt efficient and fruitful.
Data.gov is like a government-subsidized farmers’ market, offering a wide array of fresh, local produce (U.S. government data) that supports sustainable and healthy cooking (research and analysis).
EU Open Data Portal and UN Data are like visiting the central food markets in European cities and the United Nations’ food program, where you can access a rich variety of ingredients (data) contributed by countries from around the world, fostering a global culinary experience.
World Bank Open Data is like a market stall that offers free, high-quality ingredients (development data) sourced from every corner of the globe, aiming to improve the nutrition and taste of dishes (global understanding) everywhere.
ImageNet is like a photography exhibition turned into a cookbook, providing you with high-resolution images (visual data) of dishes from around the world, helping you to visually master the art of plating and presentation.
Common Crawl is akin to a relentless food critic who has visited every restaurant (website) in the world and documented their experiences in exhaustive detail, offering you insights into global culinary trends and customer preferences.
Each market (data repository) offers unique ingredients (datasets) that can help you as a chef (AI enthusiast) to refine your cooking skills (AI models), experiment with new recipes (projects), and eventually master the art of cuisine (AI applications) from around the globe. Remember, the key to becoming a master chef in the world of AI cuisine is knowing where to find the best ingredients and how to combine them creatively and responsibly.
Dilemmas
© 2024 Technorocks.com
All rights reserved