Top 12 Best Machine Learning Datasets for Practicing

 In this technological era, human-related tasks are mostly done with the help of machines. All of these things are now possible only through Machine learning and Artificial Intelligence. It is like a blessing for us that we make all our tasks easier and efficient with the help of these technologies. Nowadays it becoming the most demanding technology that everyone wants to implement this technology in his businesses/industries.  According to the report, the global machine learning market valued at $1.59 Billion in 2017 and expected to rise to $20.84 Billion in 2024. Through this report, you can assume how it will bring revolution in coming years.

 The software for machine learning is just as good as the training sets. For testing purposes, machine learning datasets are typically used. Collecting homogeneous data is a dataset. The dataset is used to train and assess the model of machine learning. Building an effective and secure infrastructure plays a vital role. If your dataset is noise-free and standard, better accuracy will be given by your system. However, At present, we're enriched with various datasets. It can be data relevant to companies, or it can be medical data, and many more. However, the actual problem is to figure out the relevant ones according to the specifications of the method.

Here let us take a look at best machine learning datasets for practicing:

 Image Datasets for Computer vision

 ImageNet: ImageNet is one of the strongest datasets of Machine Learning based on Computer Vision out there. It has more than 1,000 object types or individuals with several pictures associated with them. ImageNet's Large-Scale Visual Recognition Challenge (ILSVRC), which created many of the latest state-of-the-art Neural Networks, even faced one of the biggest ML challenges.

Google’s Open Image: 9 million URLs for over 6,000 types of classified public images. Under creative commons, each picture is licensed.

 Natural Langauge Processing

 Amazon Reviews: A set from the last 18 years with more than 35 million ratings. This includes items such as reviews, ratings in plain text, and user details. It also provides full information on the product for reference.

 Wikipedia Links Data: The full authority of Wikipedia including 4 million articles containing 1.9 billion words. Your search choices are diverse and include searches for both words and phrases as well as paragraph sections. 

 Facial Recognition Datasets

 The facial image dataset is based on both male and female facial images. Machine learning and deep learning algorithms can be carried out to detect gender and emotion using the facial image dataset. It has a variety of details, such as context and scale variations and variation of expressions.

UMDFaces Datasets: Both still and video photos are included. The dataset is annotated and contains over 8,000 subjects with about 367,000 faces.

 Public Government Datasets

 Data.gov: This site makes it possible for multiple US government agencies to download data. Data can vary from govt. budgets to school performance scores. However, be warned: much of the knowledge needs additional research.

EU Open Data Portal: In fields as diverse as economics, jobs, research, the environment and education, the EU Open Data Portal offers access to open data released by EU institutions.

US Healthcare Data: In this dataset, the FDA drug database and the USDA Food composition database have collected data on population health, diseases, drugs, and health plans.

Iris Datasets

Another dataset suitable for linear regression, and hence for beginner machine learning projects, is the Iris dataset. This includes details on the scale of various parts of the flowers. All these sizes are numerical, making it easy to get started and no preprocessing is needed. The target is pattern recognition, based on different sizes, classifying flowers.

Finance and Economics Datasets

World Bank Open Data: Datasets include the composition of the population and a wide range of global economic and development indicators.

IMF Data: Open data collection from the International Monetary Fund on topics such as debt rates, prices of goods, international markets and foreign exchange reserves.

Google Trends: Examine and interpret internet search activity information and trending news stories worldwide.

Financial Times Market Data: Up-to-date information, including stock price indices, commodities and foreign exchange, on financial markets from around the world.

Autonomous Driving Datasets:

Comma.ai: It includes information such as the speed, acceleration, steering angle, and coordinates of the GPS of a vehicle.

WPI Datasets: Traffic signs, pedestrian and lane detection datasets.

Oxford’s Robotic Car: More than 100 repetitions of the same road, collected over a span of a year, via Oxford, UK. In addition to long-term changes such as construction and roadworks, the dataset records various combinations of weather, traffic and pedestrians.

MIT AGE Lab: A sample of the 1,000+ hours of data obtained at AgeLab for multi-sensor driving.

Open Datasets Finder

Kaggle: A data science platform that includes a number of interesting datasets that are externally contributed. In its master list, you can find all sorts of niche datasets, from ramen scores to basketball data to even Seattle pet licenses.

Google Datasets Search: Dataset Search lets you locate datasets wherever they are hosted, whether it's a publisher's site, a digital library, or the web page of an author, similar to how Google Scholar works. It's a wonderful dataset finder, and it has over 25 million datasets in it.

UCI Machine Learning Repository: One of the web's oldest dataset sources, and a great first stop in search of interesting datasets. The vast majority are clean, but the data sets are user-contributed and thus have varying levels of cleanliness. You can download info, without registration, directly from the UCI Machine Learning repository.

MNIST Datasets

The MNIST dataset will assist you with creating your model. This dataset for Machine Learning is for picture recognition. It's a well-known and fascinating data collection for machine learning. The interesting fact of this dataset is that it provides both 60000 training and 10000 testing instances.

Boston Housing Datasets:

Based on various variables, such as number of rooms, area, crime rates and many others, the Boston House Price Dataset consists of the house prices in the Boston area. For beginners to ML seeking simple machine learning ventures, it is a great starting point, as you can exercise your linear regression skills to predict what a certain house's price should be. It is also a really common dataset for machine learning, so you can find a lot of helpful resources about it online if you get stuck.

Credit Card Fraud Detection Datasets

The dataset includes credit card purchases which are classified as fraudulent or genuine. This is important in developing a model for detecting fraudulent transactions for businesses that have transaction systems.

Youtube Datasets

This dataset contains a large-scale, labeled dataset with high-quality annotations created by the computer. It helps to use a machine-learning algorithm to create a video classification model. This dataset has enhanced the quality of annotations and machine-generated labels and has 6.1 million URLs, labeled with 3,862 visual entities in the vocabulary. All videos with one or more marks are annotated (an average of 3 labels per video).

Conclusion:

The dataset is an important part of applications for machine learning. It can be made available in various formats, such as .txt, .csv, and many more. The labeled training dataset is used in supervised machine learning, and no label is needed in unsupervised testing.

 

Also Read: Top Prerequisites to learn Machine Learning

 

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