Based on our record, Scikit-learn should be more popular than Thunkable. It has been mentiond 29 times since March 2021. 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.
OP you don't need to know coding at all to make app. Try something like App Inventor Thunkable. Source: over 1 year ago
What do you think will be the best mobile app builder no code in 2023? a) Adalo b) Flutterflow c) Moxly d) Thunkable e) Glide 2. Why do you think that will be the case? 3. What are the benefits of using a mobile app builder no code? 4. Do you have any experience using a mobile app builder no code? If so, what was your experience like? 5. Do you think more people will start using mobile app builders no... Source: over 1 year ago
Thunkable is a no-code tool designed specifically for building native mobile apps. Features include drag-and-drop components, advanced logic, native mobile app functionality, and easy publication. Thunkable apps can be directly published from the platform to the Apple App Store, Google Play Store, or the web. Source: over 2 years ago
I had ideas to build an app, and made few 2 years ago or so. Indeed these technologies are great to start with. I would suggest going with Kodular.io or thunkable.com instead of appinventor. There are many pros of using these, cuz I've personally used them to build stuff I can say go with either of the two. They are completely free to start with. Source: almost 3 years ago
For the app maybe you could use something like https://thunkable.com/. Perhaps you could try something like https://firebase.google.com/ for the backend not sure if it is to technical, not used either of the tools myself. Source: almost 3 years ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 5 days 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 / about 1 year 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
Bubble.io - Building tech is slow and expensive. Bubble is the most powerful no-code platform for creating digital products.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
MIT App Inventor - App Inventor is a cloud-based tool, which means you can create apps for phones or tablets right in your web browser.
OpenCV - OpenCV is the world's biggest computer vision library
Kodular - Much more than a modern app creator without coding
NumPy - NumPy is the fundamental package for scientific computing with Python