Based on our record, Scikit-learn seems to be a lot more popular than Nilearn. While we know about 28 links to Scikit-learn, we've tracked only 2 mentions of Nilearn. 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.
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
I don't know pyradiomics, it looks interesting. From personal experience I can also recommend the library nilearn (developed by scikit-learn core people) and nipype (and impressive interface to all neuroimaging toolboxes out there. Also, I forgot to mention sMRIprep which is fMRIprpe's little sibling but exclusively for anatomical/structural data. Plus, there's MRIQC, that can extract multiple quality parameters... Source: over 1 year ago
The toolbox that you probably might be most interested in is nilearn. It's co-developed by some guys from the scikit-learn team and contains many amazing machine learning routines. CNN might not be the only one you want to look into. Source: about 3 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
OpenCV - OpenCV is the world's biggest computer vision library
Amazon Rekognition - Add Amazon's advanced image analysis to your applications.
NumPy - NumPy is the fundamental package for scientific computing with Python
Microsoft Video API - Automatically extract metadata from video and audio files using Video Indexer. Improve the performance of your media content with Azure.
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.