UCI Machine Learning
UCI Machine learning is a blend of multiple domains related to the science field that provide insights into various parts of everyday life. The only thing behind Machine Learning is that it cannot work individually. This means that in the presence of algorithms there is a need for anything more. The thing required for getting insights by using these Machine Learning algorithms is “DATA”. No Machine Learning algorithm can generate useful results if data is not present with any of the algorithms.
What is the UCI Machine Learning Repository?
It is a huge collection of data sets to support the Machine Learning community. The Machine Learning community utilizes this Machine Learning repository for quantitative and empirical analysis by applying various techniques and algorithms of Machine Learning. This archive was established in 1987 whose founder David Aha at UC Irvine. UCI Machine Learning repository provides free access to all sorts of datasets to researchers and students. It is cited over 1000 types of research. The online version that is available nowadays is designed in 2007 for easy access. Overall, it contains data from different areas of life that include life science, physical sciences, computer science, social sciences, businesses, games, etc.
UCI Machine Learning repository presently consists of 557 different datasets related to different domains that include text analysis, recommender systems, image processing, data mining, etc. The website clearly demonstrates the Name of the dataset, Type of Dataset, Task for which data can be used, Features type, Number of records in the dataset, Number of Features in the dataset, and Year of dataset collection. Furthermore, the UCI Machine learning repository demonstrates that there are almost 417 classification tasks, 129 regression tasks, 112 clustering task, and 56 other tasks. You can read more about the UCI Machine Learning by reading list of top 10 machine learning books in 2021.
Why the UCI Machine Learning Repository?
Learners often ask these questions that I am a beginner and just finished my theoretical subject. Now one wants to make his hands dirty by practical applying Machine Learning algorithms or data science projects right from real life. Then the UCI Machine Learning repository contains real-world datasets that are small enough to excel in their skill on normal desktop or laptop. So, applying these datasets will surely make the foundation solid for a beginner. You can explore machine learning internships oppurtunities.
Moreover, new datasets are also uploaded on the website. These are the benchmark datasets. Grabbing the data right from the scratch utilizes plenty of resources. After consuming such resources still feature engineering process is too much complex. But these benchmark datasets on UCI are structured by well-known experts and can be implemented for any sort of novel idea. You can get this set of databases here.
How to use UCI Machine Learning Datasets?
After learning the theatrical techniques in Machine Learning, self-study is the best choice to use this technique. This will clear the plenty of concepts for the learner. Just visiting the website and downloading the dataset in the computer. Anyone can apply this dataset even in Weka, R, Python, Rapid Miner, or any other tool. Even a person is not familiar with the coding he/she can also perform analysis by using an easy interface tool “Weka” to perform analysis.
You can learn how to download data from UCI respiratory by following this tutorial: