Based on our record, Teachable Machine should be more popular than Scikit-learn. It has been mentiond 55 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.
Not sure if I've seen anything of the sort, seems rather specific. Maybe try a Teachable Machine project? https://teachablemachine.withgoogle.com/. - Source: Hacker News / about 1 month ago
Train a computer to recognize your images, sounds, and poses. Use this resource to gain a better understanding. - Source: dev.to / 5 months ago
We will create an machine learning model that can classify Arabic and English books. To collect, train, and test data, we will use Teachable Machine from Google. - Source: dev.to / 9 months ago
a lot of places! But for a high schooler, better to focus at what you want to do first. Or if you still haven't gotten any idea, try a simple explanation on what ml is without the math on youtube and tinker around a no code machine learning platform like https://teachablemachine.withgoogle.com/. Source: almost 1 year ago
The principle is roughly the same as it is possible with teachablemachine.withgoogle.com. Source: about 1 year 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 / 2 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: 12 months 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
Google Cloud Machine Learning - Google Cloud Machine Learning is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
OpenCV - OpenCV is the world's biggest computer vision library
NumPy - NumPy is the fundamental package for scientific computing with Python
AngryTools Online Gradient Generator - Angrytools - Online CSS Gradient Generater interface to generate cross browser CSS gradient code as well as Android gradient code. generator produce linear or radial gradients that can be used in your web page design or android apps.