Jovian
Amazon Machine Learning
Deepnote
Machine Learning Playground
ML Showcase
Apple Machine Learning Journal
Lobe
Apple
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
Exploratory
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Jovian Scikit-learnBased on our record, Scikit-learn seems to be a lot more popular than Jovian. While we know about 28 links to Scikit-learn, we've tracked only 2 mentions of Jovian. 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.
Last Month I did a webscraping project after learning from the C.E.O of Jovian, how to webscrape data from websites using python programming. - Source: dev.to / about 2 years ago
I am interested in Data analysis and machine learning did some simple bootcamp from jovian.ai but yeah the story goes the same (i don't even where to start in kaggle's first competition even though I completed the ml bootcamp ). Source: over 2 years 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: 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
Amazon Machine Learning - Machine learning made easy for developers of any skill level
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Deepnote - A collaboration platform for data scientists
OpenCV - OpenCV is the world's biggest computer vision library
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
NumPy - NumPy is the fundamental package for scientific computing with Python