This article is also available in Japanese and Simplified Chinese. Gengo.ai has assembled a wealth of resources for machine learning and natural language processing activities. In our previous articles, we explained why datasets are such an integral part of machine learning and natural language processing. Without training datasets, machine learning algorithms would have no way of learning how to do text mining, text classification, or categorize products. So far, Gengo.ai has compiled lists of the best open datasets by industry.

Apple transformed the cell phone industry with the release of the first-generation iPhone in 2007. At the time, Steve Jobs described the first-generation iPhone as an "iPod, a phone and an Internet communicator." The first-generation iPhone included the basic functions of calling and text messaging, browsing the internet, and listening to music. But it did not have a video recording function, front-facing camera, or GPS, and at the time in 2007, the most popular smartphone applications today had not yet been created, such as Instagram and Uber. Following the first-generation iPhone, Apple and its competitors have continued to offer innovative and helpful features and applications on each new smartphone model.

While artificial intelligence (AI) technology is making headlines in a wide range of industries, legal AI may not come to mind for many. However, AI is already transforming the legal sector in many ways, primarily because it is streamlining traditionally cumbersome processes and allowing professionals to focus on higher-level tasks. In case you missed our previous dataset compilations, you can find them all here. Still can't find the custom data you need to train your model? Gengo provides custom AI training data in 37 languages for your specific machine learning project needs. Contact us to learn more about how Gengo can work for you.

This article is also available in Japanese and Simplified Chinese. What are some open datasets for machine learning? We at Gengo decided to create the ultimate cheat sheet for high quality datasets. First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally-contributed interesting datasets.

Machine learning projects can range from small datasets and standard algorithms, to much larger projects that use neural networks engines with massive datasets. So how does one get started? What you need is a data pipeline that brings in all your components working together. A data pipeline is a set of actions that extracts data, then helps build predictions and finally output. The following is an illustrated diagram of the two types of machine learning data pipelines, featuring the best tools to use for each step of the process.