While we perform research in machine learning, selecting a dataset efficiently affects the results and relevancy of the research. Morden methodologies by merging of various algorithms are done to get the desired solutions. Here, we list famous datasets that are usually applied over various fields in machine learning research. Let’s start!

1. General Machine Learning

• UCI Machine Learning Repository: Machine learning society extensively accomplishes the collection of databases, field theories and data generators.
• Kaggle Datasets: It acts as a platform for introducing several datasets on a broad area of topics, where we are capable of cooperating in competitions.
• Google Dataset Search: This tool permits us for exploration of datasets that are gathered over the web.

2. Image Processing and Computer Vision

• ImageNet: We create a huge visual database to deploy in visual object recognition software research.
• COCO (Common Objects in Context): This involves methods like large-scale object detection, segmentation, and captioning dataset.
• MNIST: It is a database of handwritten digits that is generally applied for training different kinds of image processing systems.
• Pascal VOC: Object detection, classification and segmentation are the approached datasets for Pascal VOC.
• CelebA: These datasets are huge-scale face attributes with various celebrity images.

3. Natural Language Processing

• Stanford Natural Language Inference (SNLI) Corpus: The collections of labelled sentence pairs are designed by us for performing Natural Language Processing (NLP) tasks.
• GLUE Benchmark: It is a gathered resource for training, estimating and observing the natural language understanding systems.
• Common Crawl: The substance of web crawl data is collected across 25 billion web pages.
• 20 Newsgroups: These are the group that contain around 20,000 newsgroup documents and splitted over 20 different newsgroups.

4. Audio and Speech Recognition

• LibriSpeech: Through this, we gather arounds 1,000 hours of English speech which is extracted from audiobooks.
• UrbanSound Datasets: This is a pack of urban sounds for the improvement of programming audio classification.
• Voice Dataset by Mozilla Common Voice: It is the open-source dataset and it consists of a multi-language dataset of voices for helping us to train speech-activated applications.

5. Time Series Analysis

• Yahoo Finance: This finance assists various companies and time ranges with their stock market data.
• UCR Time Series Classification Archive: We employ the pack of time series datasets for performing classification tasks.

6. Healthcare

• MIMIC-III (Medical Information Mart for Intensive Care): It is a huge and single-eye database that is accomplished by us for responsive information associated with admitted patients to crucial care units.
• Cancer Imaging Archive: This contains major records of medical images of cancer that allow the public to download it.
• NIH Chest X-ray Dataset: It is a huge-scale dataset including 112,120 frontal-view X-ray images of 30,805 unique patients.

7. Autonomous Vehicles and Robotics

• KITTI Vision Benchmark Suite: For working autonomous driving tasks, we suggest this dataset. The autonomous driving tasks like stereo, optical flow and visual odometry etc..,
• Waymo Open Dataset: These datasets are a great capacity multimodal dataset especially for autonomous driving.

8. Recommender Systems

• MovieLens: We collect a series of datasets consisting of movie ratings, metadata and recommendation data.
• Amazon Reviews: The amazon reviews are involved in this dataset, ranging from May 1996 – July 2014.

9. Social Media Analysis and Sentiment Analysis

• Sentiment140: In this analysis, our dataset contains 160,000 tweets derived deploying the Twitter API.
• Yelp Dataset Challenge: This incorporates feedback, user information, business data and report data for shout.

10. Government, Economics, and Social Sciences

• World Bank Open Data: This is free of cost and open access to worldwide development data.
• FBI Crime Data Explorer: Datasets are distributed based on crime over the U.S.

Remember before utilizing any dataset, ensure to verify the license and conditions of use to make sure that we are agreeing with the dataset’s legal and moral limitations. Furthermore, examine the size and capacity of the dataset as it relates to our specific research questions and bear in mind that pre-process our model which is consistent to the requirements of our studies.

Machine Learning Innovative Dissertation Ideas

Choosing a specific thesis topic that captivates readers interest is difficult. Have a look at the current work of our team, contact us if you are looking for expertise solutions, we will guide you in all possible ways. Get a hard start on your writing with the aid of our team.

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