Based on our record, Scikit-learn seems to be a lot more popular than ML.NET. While we know about 27 links to Scikit-learn, we've tracked only 2 mentions of ML.NET. 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.
Documentation - You can find tutorials and how-to guides in our documentation site. Probably the easiest way to get started is with the Model Builder extension in Visual Studio. Here's install instructions and a tutorial to help you start out. Source: almost 2 years ago
I would start right here- ML.Net Documentation. Source: almost 3 years 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: 12 months 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
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
R Caret - Documentation for the caret package.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
H2O.ai - Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.
OpenCV - OpenCV is the world's biggest computer vision library
R MLstudio - The ML Studio is interactive for EDA, statistical modeling and machine learning applications.
NumPy - NumPy is the fundamental package for scientific computing with Python