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Also, many tools only talk SQL. Shillelagh was developed for Apache Superset, a powerful open source business intelligence web application, and allows it to query an infinitude of new data sources without having to change a single line of code in Superset. Source: almost 2 years ago
I'm adding this to Apache Superset today! Source: almost 2 years ago
I also like to do some data analysis on the side and recently ran across Apache Superset which describes itself as a "modern data exploration and data visualization platform". Coincidentally, Superset has a lot of Python code and can be deployed in containers (nine of them at current count!). - Source: dev.to / about 2 years ago
Please don't hesitate to like and bookmark this post, write a comment, and give a star to Cube and Superset on GitHub. I hope these tools would be a part of your toolkit when you decide to build a metrics store and a business intelligence application on top of it. - Source: dev.to / over 2 years ago
Discussion by kgabryje at apache / superset “feat(native-filters): add search all filter options #14710“. - Source: dev.to / almost 3 years ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 6 days 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 / 4 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 / about 1 year 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: about 1 year 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
Mage AI - Open-source data pipeline tool for transforming and integrating data.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Aha! Visual Chart Tool - Create beautiful product roadmap visualizations
OpenCV - OpenCV is the world's biggest computer vision library
Apache Superset - modern, enterprise-ready business intelligence web application
NumPy - NumPy is the fundamental package for scientific computing with Python