Machine learning and AI technology have exploded in capabilities and applications in the past couple of decades. But until very recently, you still had to be a data scientist and computer engineer to truly get a handle on teachable machines.
Thankfully, the market has begun to flood with machine learning tools and platforms for the non-technical, non-programmers among us. These SaaS tools offer the same computing power as AI giants, like Google and Apple, but with no coding skills required.
Also read about SQL programming to know more about the language.
No-code AI platforms make machine learning accessible to everyone – some simply plug and play and some allow you to train advanced models to your specific needs.
Let’s take a look at some of the best no-code machine learning tools, and then we’ll show you how to build your model, also without coding.
- No-Code Machine Learning Tools
- No-Code Machine Learning Tutorial
- No Code Machine Learning Tools
Take a look at these top no-code AI platforms.
- MonkeyLearn | All-in-one powerful text analytics and visualization
- Create ML | Use existing Apple data to train models
- AI | Dive right in for quick results
- Fritz AI | Add ML and augmented reality to your app
- Google AutoML | Harness the power and experience of Google
- RunwayML | Use machine learning to make new things
- MakeML | Out-of-the-box computer vision
- Teachable Machine | A user-friendly Google option
Read more about the statistics of machine learning.
MonkeyLearn offers a suite of powerful no-code machine learning and AI tools to analyze text from internal CRM systems, social media, emails, documents, online reviews, and more. MonkeyLearn’s tools provide real-time analysis for immediately actionable insights and data-driven decisions.
MonkeyLearn is completely scalable and you can implement pre-trained models straightaway or train your own to your specific needs and criteria, usually in just a few steps. Download the Edureify app now to explore more things.
Built on top of macOS with the same machine learning architecture as Apple Photos and Siri, Create ML’s machine learning platform is ideal for regular Apple users because it can be easier and faster to train natural language processing (NLP) and image classification models with your data that’s already stored on your Mac or in Apple’s cloud.
Drag and drop tools make model training easy, and you don’t have to be an iOS developer to fine-tune the metrics and make their powerful artificial intelligence work for you. Real-time data validation allows you to check your training progress in a non-technical manner, and once it’s trained to your liking, you can integrate it with iOS applications right away.
Arnett users from data gathering to machine learning analysis in just a few clicks – upload a CSV file, choose the data analysis you need, and see your results right away, including responses to direct questions using NLP and NLU (natural language understanding).
AI promises to find the right algorithm for any use case, so the training is streamlined and models can be put to use right away, although the level of customization can be a bit lower than other platforms. However, their “what-if” scenarios can show actionable insights within just a few minutes of getting started – ideal for non-programmers. The Bootcamp coding courses at Edureify can help you in gaining access to the best courses at an affordable price.
Fritz AI is designed primarily to help smartphone app designers (iOS and Android) integrate quick and easy machine learning tools without the need for a huge background in data science. Many of the integrations do require a fair amount of code, as they are used in software development. However, certain e-commerce and augmented reality solutions can be implemented with no-code tools. For example, style transfer to mimic images or pose estimation to display your clothing on a virtual, movable model.
Google AutoML allows users to harness the power of artificial neural networks to build effective predictive models with regular text and image data, and integrations with Google Sheets, Google Slides, and more, make it easy to get started.
AutoML functions completely in the cloud, so you don’t need any infrastructure to get started. Google’s advanced analytics offer image classification, NLP analysis, AutoML translation, and video intelligence.
Google’s decades of experience with ML models mean their pre-trained models are often perfectly usable right out of the box, and their UX makes training custom models relatively pain-free for the AI uninitiated. Do explore the Bootcamp coding courses at Edureify now.
RunwayML offers a user-friendly desktop interface that aims to “make machine learning more inclusive,” to offer fun, just as much as client acquisition. Their image-centric tools allow makers/artists/creators to harness the power of AI and machine learning to push their ideas forward – it’s about experimenting with the tools to find out what they can do.
Use their off-the-rack tools to improve image resolution and remove backgrounds or create wholly new images – from their bank of sample templates – with generative adversarial networks (GAN).
make use of advanced computer vision techniques to analyze images and video much like the human brain does – enabling programs to recognize and analyze images in real-time. make offers some of the most advanced ready-to-use computer vision machine learning software, like “ball detection,” that can be trained to individual needs with just a few clicks and drags.
Another Google offering, Teachable Machine is even more friendly to the non-coder, offering in-browser machine learning model training to identify and classify text and images. When it comes to image recognition, Teachable Machine may be the most easy-to-use no-code machine learning platform of all. Try the Bootcamp coding courses at Edureify now.
Learn how to train your machine learning model with no code in just four steps, then put it to work analyzing your data right away.
No-code platforms have made machine learning just as accessible to start-ups as it is too huge corporations. Machine learning tools with no code are an excellent way to streamline processes, get the data you need, and make data-driven decisions with very little human interaction.
Frequently Asked Questions (FAQs)
Question:- Can I learn ML without coding?
Ans:- Traditional Machine Learning requires students to know software programming, which enables them to write machine learning algorithms. But in this groundbreaking Udemy course, you’ll learn Machine Learning without any coding whatsoever. As a result, it’s much easier and faster to learn!
Question:- Is DataRobot no code?
Ans:- The DataRobot No Code AI App Builder allows you to quickly turn any model into an AI application, without requiring any coding. This makes it much easier for business users and information workers to leverage predictions generated by their models and make more informed AI-driven decisions.
Question:- What is no code ML?
Ans:- No-code ML is a subset that tries to make ML more accessible. To deploy AI and machine learning models, no-code ML involves adopting a no-code development platform with a visual, code-free, and frequently drag-and-drop interface.
Question:- What does AutoML do?
Answer:- Automated machine learning (AutoML) is the process of applying machine learning (ML) models to real-world problems using automation. More specifically, it automates the selection, composition, and parameterization of machine learning models.
Question:- Are no-code apps good?
Ans:- No-code platforms are the best choice for building simple apps and solutions. They can’t be used to create more complex or sophisticated products and hence you can’t rely on them for each of your projects.