Are YOU a Fresher in AI, machine learning and data science or just a non-technical with no knowledge or any experience in coding, this post is for you.
Organizations primarily depend these days on the two domains — artificial intelligence (AI) and machine learning (ML) in order to build and deploy different kinds of models for running smooth operation of their business.
In this post, we list down 13 such tools which can be used to develop models without having any programming experience.
The list is in no particular order.
Below are the top 10 Best excellent tools for AI ML & data science for non-programmers which any one can take full advantage.
TOP 13 TOOLS FOR ML & DATA SCIENCE WITHOUT CODING IN 2020
DataRobot delivers trusted AI technology and ROI enablement services to global enterprises competing in today’s Intelligence Revolution.
Gartner positioned DataRobot as a Visionary in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms based on its ability to execute and completeness of vision.
WebLink Automated Machine Learning — https://www.datarobot.com/platform/automated-machine-learning/
DataRobot combines a trusted enterprise AI platform and a trusted AI-native strategic partnership for global enterprises that want to harness the power of AI and their existing teams to succeed in today’s Intelligence Revolution.
RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment.
More than 600,000 analytics professionals use RapidMiner products to drive revenue, reduce costs, and avoid risks.
- Seamlessly integrated & optimized for building ML models
- Design ML models using a visual workflow designer or automated modeling
- Deploy & manage models and turn them into prescriptive actions
Machine Learning made beautifully simple for everyone. Take your business to the next level with the leading Machine Learning platform.
BigML is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modeling tasks.
BigML is helping thousands of analysts, software developers, and scientists around the world to solve Machine Learning tasks “end-to-end”, seamlessly transforming data into actionable models that are used as remote services or, locally, embedded into applications to make predictions.
Implementing and consuming Machine Learning at scale are difficult tasks. MLbase is a platform addressing both issues, and consists of three components — MLlib, MLI, ML Optimizer.
- ML Optimizer: This layer aims to automating the task of ML pipeline construction. The optimizer solves a search problem over feature extractors and ML algorithms included in MLI and MLlib. The ML Optimizer is currently under active development.
- MLI: An experimental API for feature extraction and algorithm development that introduces high-level ML programming abstractions. A prototype of MLI has been implemented against Spark, and serves as a testbed for MLlib.
- MLlib: Apache Spark‘s distributed ML library. MLlib was initially developed as part of the MLbase project, and the library is currently supported by the Spark community. Many features in MLlib have been borrowed from ML Optimizer and MLI
Google Cloud AutoML
Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.
Use Cloud AutoML’s simple graphical user interface to train, evaluate, improve, and deploy models based on your data.
Many different machine learning algorithms exist that can easily be used off the shelf, many of these methods are implemented in the open source WEKA package. However, each of these algorithms have their own hyperparameters that can drastically change their performance, and there are a staggeringly large number of possible alternatives overall.
Auto-WEKA considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters, going beyond previous methods that address these issues in isolation. Auto-WEKA does this using a fully automated approach, leveraging recent innovations in Bayesian optimization.
Auto-WEKA will help non-expert users to more effectively identify machine learning algorithms and hyperparameter settings appropriate to their applications, and hence to achieve improved performance.
IBM Watson Studio
With Watson, you can help automate decisions and processes, and optimize employees’ time to focus on higher value work.
With an AI-powered, natural language interface developed with Watson Assistant, Watson Studio, and Watson Machine Learning, FOX Sports commentators can instantly pull up-to-the-minute trends and stats to equip themselves with the insights they need — when they need them.
Moving beyond experimentation and maintaining production-level accuracy of AI models are still big challenges. Watson Machine Learning can accelerate the time to value of any model, with a projected ROI.
Tableau allows us to create dashboards that provide actionable insights and drive the business forward.
Tableau has a mapping functionality, and is able to plot latitude and longitude coordinates and connect to spatial files like Esri Shapefiles, KML, and GeoJSON to display custom geography.] The built-in geo-coding allows for administrative places (country, state/province, county/district), postal codes, US Congressional Districts, US CBSA/MSA, Area Codes, Airports, and European Union statistical areas (NUTS codes) to be mapped automatically. You can group geographies to create custom territories] or use custom geocoding to extend existing geographic roles in the product. Source: wiki
Trifacta accelerates data cleaning & preparation with a modern platform for cloud data lakes & warehouses.
Ensure the success of your analytics, ML & data onboarding initiatives across any cloud, hybrid and multi-cloud environment
Trifacta’s mission is to create radical productivity for people who analyze data. They are deeply focused on solving for the biggest bottleneck in the data lifecycle, data wrangling, by making it more intuitive and efficient for anyone who works with data.
KNIME, the Konstanz Information Miner, is a free and open-source data analytics, reporting and integration platform.
KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.
It is free platform for drag-and-drop analytics, machine learning, statistics, and ETL
Datawrapper empowers everyone to create digitally optimized charts, maps, or tables. It’s as easy as following four steps.
Datawrapper works on the web. No need to install or update anything.
You can access your charts from any computer on this planet, and every change you make to your charts, maps and tables is saved automatically.
Visualr is single platform for multidimensional users working securely and colloboratively.
Simply upload your Excel, Access, CSV or even Flat files and start visualizing
Design extremely attractive dashboards at the snap of fingers
Highest level of security ensured while processing your data metrics.
Paxata provides a self-service data preparation solution for business and technical teams to visually clean, integrate, and govern data at scale.
Paxata is a visually-dynamic, intuitive solution that enables business analysts to rapidly ingest, profile, and curate multiple raw datasets into consumable information in a self-service manner, greatly accelerating development of actionable business insights.
Paxata also provides a rich set of workload automation and embeddable data preparation capabilities to operationalize and deliver data preparation as a service within other applications.
The Paxata Adaptive Information Platform (AIP) unifies data integration, data quality, semantic enrichment, re-use & collaboration, and also provides comprehensive data governance and audit capabilities with self-documenting data lineage.