5 Machine Learning Tools for people who don’t know programming
With the recent boom in machine learning and artificial intelligence, a lot of young people are coming into programming with the slightest idea about coding. The good news is there is a way to become an expert in machine learning, irrespective of one’s programming abilities. Some tools provide simple, user-friendly GUI (Graphical User Interface) to create high-quality machine learning models for anyone with minimal knowledge of algorithms. We’re going to present five such tools in this post for people who don’t know to program.
RapidMiner (RM), first launched as independent open-source software called Rapid-I in 2006, covers the entire life cycle of prediction modeling, from data preparation to model construction and finally validation and deployment. RapidMiner provides a non-data scientist with full transparency and governance for machine learning techniques. This platform offers an active GUI, data mining, and machine learning. The user can share and re-use predictive models, automate processes, and use models in production using the RapidMiner Server.
DataRobot (DR) has been built by Jeremy Achin, Thoman DeGodoy, and Owen Zhor on a highly automated machine learning platform. It allows users to create highly accurate predictive models with complete transparency quickly and easily. The coding and machine learning skills on this platform are optional, but only information is essential. It automates almost 80% of the tasks of experienced data scientists. DataRobot detects the best data processing and feature set automatically. Even hyperparameters are selected automatically based on the score and error metric for the validation.
BigML provides an excellent GUI that takes the user through a step-by-step process, starting with sources, datasets, models, and ending with predictions, sets, and evaluation. BigML algorithms make solving machine learning problems such as classification, regression, combination, clustering, and discovery extremely easy. This highly scalable cloud-based machine education service can be used seamlessly for integration as well as for application data-driven decision-making. BigML recently introduced a new feature known as organizations to ensure that companies adopt machine learning throughout their corporate structure.
Google Cloud AutoML
Cloud AutoML is part of the offerings of Google’s Machine Learning suite that allows people with limited ML expertise to create high-quality models. Built on machine learning algorithms such as learning transfer and search technology for neural architecture, it has a drag-and-drop interface. It allows the users to upload images, train the model, and then deploy those models directly to Google Cloud. The suite includes AutoML Vision deriving insights from images, AutoML Natural Language helps to gain insights from text, AutoML Translation detecting and translating between different languages.
Driverless AI, H2O.ai’s automated machine learning platform, requires no data scientist to provide unique and advanced data visualization, feature engineering, model interpretability, and low latency deployment functionality. This platform takes a raw dataset and visualizes interesting data exploration patterns automatically. To increase accuracy, it then applies automatic feature engineering. Tt auto-tunes model parameters and the model that yields the best results are provided to the user. It also has excellent features during the training process to interpret the model along with a panel for ranks of real-time feature importance.
This article was originally published on roboticsBiz. Republished with permission