Datasets

Datasets used in machine learning are important in order for algorithms to learn from them. In machine learning, datasets are used to make predictions, with labels representing the outcome of each prediction (success or failure).

There are three general types of machine learning methods: supervised (learning from examples), unsupervised (learning through clusters), and reinforcement learning (rewards). In supervised learning, a computer is taught to recognize patterns in data by observing its behavior. Supervised learning algorithms include random forests, nearest neighbors, weak laws of large numbers, ray tracing algorithms, and support vector machines.

Datasets for machine learning can be obtained in a variety of formats and from a variety of sources. Datasets used in machine learning can be classified into three categories: textual data, image and video data, numerical data, and audio data.

22 Reliable Dataset Storages for Machine Learning in 2023

The list of 22 the best and new open dataset finders that you can use to browse through a wide variety of niche-specific datasets for your data science projects.

*Organic monthly visits column is the research in Semrush tool to find new opportunities.
Hypothesis: less visits are the fact of less usage a data from this storage.

Based on historical data, a dataset is a collection of information that can be used to predict future events or outcomes. Before machine learning algorithms use datasets, they are typically labeled so that they are aware of what outcome it should predict or classify as an anomaly.

The machine learning algorithm may learn from past data if you label your dataset "churned" and "not churned" if you are trying to predict whether a customer will churn. Even unstructured data can be used to generate machine learning datasets. The tweets mentioning your company, for instance, could be used as a machine learning dataset.

In order for your dataset to become usable, you must complete several steps: data collection, data preparation, and data annotation.

It is necessary to prepare a specific dataset for each industry. It is common for even the simplest of projects to require data that is unique or specific.

In the following sections, we will discuss a number of open sources of datasets. However, let's first examine the industry context of datasets.

Automotive industry datasets

Due to the rapid development of self-driving vehicles, there is a growing demand for pedestrian datasets and datasets with road signs to assist manufacturers in training their computer vision AI to recognize the correct sign or pedestrian.

This is only one direction in which automotive technology datasets are heading. Additionally, there is a dataset devoted to safety questions and consists of data pertaining to human behavior (driver) while riding. An individual should be able to recognize the first signs of fatigue (the quantity of yawnings, the open or closed eyes, the posture of the person, etc.).

Service is another important aspect of the automotive industry. As a result, it is necessary to collect and annotate data on spare parts, for example.

Medical datasets

Another industry with a high demand for datasets is medicine. In terms of healthcare data, images account for the majority (nearly 90 percent). As a result, there are numerous opportunities for training computer vision algorithms in order to meet healthcare needs. X-Rays, CTs, and MRIs are the most common types of medical image data generated in radiology departments.

When it comes to healthcare data, the labeling must be performed by medical professionals. Compared with annotators without domain expertise, their services cost significantly more per hour. As a result, this presents another barrier to the generation of high-quality medical datasets.

Despite the fact that medical data is abundant, getting it ready for machine learning usually takes more time and money than average in other industries. This is due to the stringent regulations and the need to engage highly-paid domain experts. Thus, it is not surprising that publicly available health datasets are relatively rare and attract the attention of researchers, data scientists, and companies focused on developing medical artificial intelligence solutions.

Retail datasets

The term "retail data" refers to any information that retailers can collect about their business, which can be used to improve and secure their business.

Data is available in a variety of forms, including point-of-sale, loyalty card, and market data.

In addition, there are customer-centric data, supply chain and operations data, and merchandising data to consider. For the business to succeed in the future, it is crucial to take advantage of all this data.

The main goal is to predict consumer behavior, whether it's buying or stealing.

Waste Management

An efficient waste management system relies on the use of technology and data. Smart waste management uses IoT (Internet of Things) technology to optimize resource allocation, reduce operating costs, and increase sustainability.

In order to improve the reusing and recycling of waste, automatic waste segregation machines use a variety of sorting methodologies to remove organic matter, plastics, metals, bricks, and stones from garbage as much as possible. So, there is a huge potential to train computer vision systems with the correct datasets.

What are the sources of ML datasets?

It is difficult to provide a definitive answer to this question. In order to source a dataset, it is necessary to understand the requirements and scope of the project. There are a number of datasets that can be obtained from vendors for various types of projects if your project does not require highly personalized data.

Whenever machine learning models require unique or specific data, which is not available in free space or is not enough - there are trusted data collection and creation services available.