Introduction
Technology has been evolving very expeditiously over the past decade. These advancements have set off a trend for learning with technology. To satisfy the learning needs, people are embracing self-directed learning. It is important to mention that as the world is preparing for the Fourth Industrial Revolution (I4.0), the workforce has to keep up with the advancements in technology. At the same time, there has been quite a buzz around the Machine Learning and Artificial Intelligence that forms the heart and soul of the I4.0. In other words, learning Machine Learning is the need of the hour.
Now that it is imperative to learn Machine Learning, there are three success mantras of mastering it: PRACTICE, PRACTICE, and PRACTICE. But the basic question that comes up in our mind is, what to practice on. A true dataset should be available to work on as if dealing with a real ML problem. In this blog, we will be discussing some of the most popular data repositories for extracting sample datasets for mastering Machine Learning skills.
Data, DataSet, and Databases
Before we begin, it’s important to clear the air by defining the basic definitions related to datasets.
What is data?
- Data is a collection of information that is based on certain facts.
What is a dataset?
- Dataset is a structured collection of data.
What is a database?
- The database is an organized collection of multiple datasets.
The data which is used can be collected from various sources such as experimentations, surveys, polls, interviews, human observations, etc. It can also be generated by machines and directly archived into databases.
DataSets For Machine Learning Projects
The choice of data collection is a very crucial step in the success of the Machine Learning program. The source of the datasets is equally important, as it is a matter of the reliability and trueness of the collected data. Some of the most popular data repositories that are required for acquiring Machine Learning datasets are discussed below.
KAGGLE
This platform is owned by Google LLC and is a repository of huge data sets and code that is published by its users, the Kaggle community. Kaggle also allows its users to build models with the Kaggle datasets. The users can also discuss the problems faced in analyzing the data with its user community.
Kaggle also provides a platform for various open-source data Science courses and programs. It is a comprehensive online community of Data Science professionals where you can find solutions to all your data analytics problems.
UCI MACHINE LEARNING REPOSITORY
UCI Machine Learning repository is an open-source repository of Machine Learning databases, domain theories, and data generators. This repository was developed by a graduate student, David Aha, at the University of California, Irvine (UCI) around 1987. Since then, the Centre for Machine Learning and Intelligent Systems at the UCI is overseeing the archival of the repository. It has been widely used for empirical and methodological research of Machine Learning algorithms.
QUANDL
Quandl is a closed-source repository for financial, economic, and alternative datasets used by analysts worldwide to influence their financial decisions. It is used by the world’s topmost hedge fund, asset managers, and investment banks.
Due to its premiere and closed-source nature, it cannot be used for just practicing Machine Learning algorithms. But citing its specialization in financial datasets, it is very important to include Quandl in this list. Quandl is owned by NASDAQ, American Stocks Exchange based in New York City.
WHO
World Health Organisation (WHO) is a specialized agency of the United Nations Organisation headquartered in Geneva, Switzerland. It is responsible for monitoring international health and continually collects data related to health across the world. WHO has named its repository of data as Global Health Observatory (GHO). The GHO data repository collects and archives health-related statistical data of its 194 member countries.
If you are looking for developing Machine Learning algorithms on health-related problems, GHO is one of the best sources of data collection. It is a repository of a wide variety of information ranging from a particular disease, epidemics, and pandemics, world health programs, and policies.
GOOGLE DATASET SEARCH
Google dataset search is a search engine for datasets powered by Google. It uses a simple keyword search to acquire datasets hosted in the different repositories across the web. It hosts around 25 million publicly available datasets to its users. Most data in this repository is government data besides a wide variety of other datasets.
AMAZON WEB SERVICES (AWS)
Amazon Web Services is known as the world’s largest cloud services provider. AWS has a registry of datasets that can be used to search and host a wide variety of resources for Machine Learning. This repository is cloud-based, allowing users to add and retrieve all forms of data irrespective of the scale. AWS also enables data visualization, data processing, and real-time analytics to make well-informed decisions driven by data.
Conclusion
The human resources are prepping up for Workforce 4.0 by constantly acquiring new skills. Machine Learning is one of the most indispensable skills for tomorrow’s workforce. In today’s world of the digital revolution, information is available at our fingertips. The datasets for Machine Learning are also available as open-source and could be utilized to build algorithms for making informed decisions.
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