Datasets are an integral component of machine learning. They aid AI developers and engineers in training models and assessing their performance.
Datasets come in all shapes and sizes, making it essential to select the one best suited for your project. In this article, we’ll highlight some of the most popular and useful datasets available for machine learning projects.
UC Irvine Machine Learning Repository
If you are interested in machine learning and need some real-world data to practice on, the UC Irvine Machine Learning Repository is an ideal resource. It is user friendly and offers a range of datasets you can use as starting points for structuring your self-study program.
The UCI Machine Learning Repository is an accessible database of machine learning problems hosted and maintained by the Center for Machine Learning and Intelligent Systems at UC Irvine. With over 25 years of existence, this repository has become the go-to place for machine learning researchers and practitioners seeking datasets.
There are over 500 datasets to select from, spanning topics and levels of complexity. You can search the data based on its characteristic (tabular, time-series, sequential or text) or the associated tasks (classification, regression or clustering) that interest you.
With so many datasets available, selecting the ideal one can be daunting. This makes it challenging to locate an effective starting point for your machine learning studies.
By adhering to some guidelines, you can find high-quality and interesting datasets to practice on that will give you a strong foundation in machine learning. These datasets are real world, have been carefully designed, and are openly accessible for everyone to use.
This task offers you the unique chance to show off your creativity and innovation through application of WEKA data mining software, by designing useful visualization and data mining solutions presented as an analytics report using selected datasets from UC Irvine Machine Learning Repository. Furthermore, it will give you a chance to apply what skills you’ve acquired during this assignment to real-life projects.
MIT Computational Physiology Dataset
The MIT Computational Physiology Dataset is an expansive repository containing de-identified health records on 40,000 critical care patients. This vast repository includes demographics, vital signs, laboratory tests, medications and more – making it useful for various machine learning tasks such as anomaly detection and predictive modeling.
From among the many ECG databases available, the MIT-BIH Arrhythma database is an invaluable source for training and testing models of arrhythma detection. It contains ECG waveforms of 48 subjects – 23 healthy and 25 with clinically significant arrhythmas – which have been selected to include in clinical practice. This dataset has been widely used to train models that differentiate single-heartbeat (morphological irregularities) from rhythmic arrhythmas.
Raj and Ray’s study yielded an AUC of 0.963 using the Discrete Orthogonal Stockwell Transform as a feature extraction method on the MIT-BIH Arrhythmia dataset. Other researchers, such as Li et al. (2017) and Qin et al. (2017) also utilize this data to train models for morphological arrhythmias.
However, the MIT-BIH dataset has some limitations. All heartbeats were recorded from 48 people, which cannot accurately represent normal human heart rhythm. Furthermore, the beat segmentation methods employed to obtain these data cannot be generalized.
In our previous study, we selected the CMUH dataset to conduct similar experiments as MIT-BIH but using resampled ECG data from this set at 360 Hz. This resampling method significantly improved model accuracy.
Our experiments involved selecting 10,000 normal ECG data and 5,000 abnormal ECG data randomly for each training set, as well as randomly selecting the test set. Then, we tested four kinds of machine learning algorithms to detect outliers; the GMM algorithm proved most successful among these models while OCSVM, iForest and LOF showed poorer performance.
Berkeley DeepDrive BDD100k
Berkeley DeepDrive BDD100k is one of the largest and most diverse open driving datasets available, featuring over 100,000 videos shot by Nexar vehicles across various environments, at various times of day, under various weather conditions.
This is a major advantage for research projects involving autonomous driving and computer vision, as it aids in creating robust perception algorithms.
Data management has been a daunting issue for deep learning engineers for years. To address it, they’ve dedicated significant effort into creating custom datasets and online annotation tools.
But this all changed when Berkeley DeepDrive released their massive data set. This global collection contains almost one million cars, 300k street signs, 130k pedestrians and much more – making it the world’s largest dataset.
It contains an extensive set of annotations, such as lane markers, object bounding boxes and full-frame instance segmentation. Furthermore, its diversity in geographic, environmental and weather factors is critical when training models.
Another major advantage is the fact that it was shot in multiple locations throughout the US, such as New York, San Francisco and The Bay Area. This gives researchers a diverse set of scenarios and scenes which will aid them in creating an improved self-driving system.
The UC Berkeley DeepDrive BDD100k is the largest and most diverse driving dataset in existence, providing researchers with large-scale video footage suitable for computer vision training. Compiled by tens of thousands of drivers from across America in various environments, this footage offers researchers an unprecedented resource.
This data set can be utilized for a variety of applications, including object detection, lane-line detection and vehicle tracking. With its various annotations, it provides an invaluable resource to help achieve high performance in these fields.
Open Data Portal
The Open Data Portal is an invaluable resource for anyone interested in machine learning. It offers numerous helpful videos and tools to get you started quickly.
It is also an excellent resource for discovering untapped machine learning datasets. The website features a search box to quickly identify the best sources, with data organized by topic.
Machine learning success depends on having high-quality data to work with. These datasets serve as the fuel that powers the algorithms, and without them they won’t run smoothly.
To make the process simpler, we’ve compiled a list of the best places to find machine learning datasets. Whether you need data for image processing, computer vision or deep learning, these sources are all free and easy to access.
Another source for machine learning data is GitHub, which hosts several data-related communities. As an engineering and data science platform, you’ll find plenty of helpful information there that can assist in creating your own machine learning systems or projects.
The UCI Machine Learning repository is an excellent source of clean machine learning datasets. It boasts a vast collection of data sets for univariate and multivariate time series analysis, classification, regression models and recommendation systems.
You may want to check out VisualData, a website where computer vision datasets are organized by category. It’s an invaluable resource for any computer vision project.
CERN is an European organization specializing in particle physics, and their Open Data portal is truly remarkable. It contains data on over two petabytes of particle collisions.
Microsoft Research Open Data
If you are working on a machine learning project and looking for high-quality datasets, Microsoft Research Open Data is an ideal place to begin. They offer several curated and freely accessible machine learning datasets that have been utilized by researchers at Microsoft as well as across the industry.
The Microsoft Research Open Data site makes it simple to discover and explore datasets by description or characteristics, then download or copy them directly into Azure blob storage. Furthermore, the portal links directly to published research studies so users can find reliable machine learning data quickly.
One of the advantages of Microsoft Research Open Data is that all data has been carefully verified by their scientists and researchers. This guarantees you an unbiased, high-quality set of information for your machine learning projects.
This dataset offers the perfect opportunity to investigate various types of data essential for building a machine learning model, such as text, categorical and time series. Often organized according to characteristics like gender, social class or ethnicity, this type of information can simplify the data analysis process.
Additionally, this data type can provide your machine learning models with a deeper insight into people’s opinions on various topics. It also enables them to spot trends in the data and track changes over time.
Another advantage of using this dataset is its public domain status, so you don’t have to worry about legal ramifications. Under CDLA Sharing, which is a form of CDLA licensing, you are allowed to share both the dataset and any enhancements made with it as long as you provide attribution.
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