I've been using Maxima since my undergraduate (over 10 years), now with Ubuntu20.04 lts, I become a newbie of SageMath. For a small project (both symbolical and numerical), in particular, student lab activities, Maxima has been a powerful tool for analyzing and visualizing data. (The Android version is also fantastic, but the poor keyboard.)
Mathematica is always enemy/friend. (My coworkers are all Mathematica speakers.)
Scikit-learn might be a bit more popular than Maxima. We know about 28 links to it since March 2021 and only 27 links to Maxima. 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.
I think the really neat piece of software behind this is maxima (https://maxima.sourceforge.io/), a rather influential computer algebra system of ancient lineage still in use today in more place than you might think. - Source: Hacker News / 25 days ago
In the maxima computer algebra system[1] which was ancestrally based on lisp it has a single quote operator[2] which delays evaluation of something and a "double quote" (which acually two single quotes rather than an actual double quote) operator[3] which asks maxima to evaluate some expression immediately rather than leaving it in symbolic form.[4] [1] https://maxima.sourceforge.io/ [2]... - Source: Hacker News / about 2 months ago
Use wxmaxima, a free and open-source computer algebra system:. Source: 5 months ago
There are several options, here is one of them: https://maxima.sourceforge.io. Source: 12 months ago
You may use maxima cas (https://maxima.sourceforge.io/) to solve symbolic complex problems. 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
MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processing—and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.
OpenCV - OpenCV is the world's biggest computer vision library
GNU Octave - GNU Octave is a programming language for scientific computing.
NumPy - NumPy is the fundamental package for scientific computing with Python