Based on our record, Bubble.io seems to be a lot more popular than Scikit-learn. While we know about 429 links to Bubble.io, we've tracked only 28 mentions of Scikit-learn. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
2. Bubble is easy for non-coders. https://bubble.io/. - Source: Hacker News / 21 days ago
Similarly to Promises/A+, this effort focuses on aligning the JavaScript ecosystem. If this alignment is successful, then a standard could emerge, based on that experience. Several framework authors are collaborating here on a common model which could back their reactivity core. The current draft is based on design input from the authors/maintainers of Angular, Bubble, Ember, FAST, MobX, Preact, Qwik, RxJS, Solid,... - Source: dev.to / about 2 months ago
For the second category, tools like bubble, Unqork, Glide are awesome (there are a lot more of these). But the risk is to go too far, and build something that really needs to be built at a lower layer in one of these tools. The providers of course want to push every use case, but in our view these are not a replacement for traditional software, and AI-assisted programming is a better path for dev augmentation than... - Source: dev.to / 3 months ago
Bubble — Visual programming to build web and mobile apps without code, free with Bubble branding. - Source: dev.to / 4 months ago
Try bubble. I have not used it myself but I have heard it referenced as a no code solution for what you are trying to do. There is a free version. https://bubble.io. Source: 5 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 11 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
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