Based on our record, Scikit-learn should be more popular than Metabase. It has been mentiond 29 times since March 2021. 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.
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 / 8 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
I've never used Tableau, but heard a lot of hate about it. However, in my previous role, we were big fans of Metabase (https://metabase.com). You can also self-host it, which was a huge win for us. - Source: Hacker News / 4 months ago
The solution really depends on what sort of problems you are trying to solve and who your customers are. There are a fair few low-code solutions out there for reporting and data visualisation that are great for finance and marketing teams for example. e.g. https://metabase.com/ , https://evidence.dev/ For enterprise processes I'd go with Camunda (solely based on recommendations and not first hand experience).... - Source: Hacker News / about 1 year ago
Metabase | https://metabase.com | REMOTE | Full-time | Backend, Frontend, Full Stack, and DevOps engineers. - Source: Hacker News / over 1 year ago
With a few simple steps, you can deploy Metabase on Microsoft Azure using Azure Container Apps. This process works for any Docker container hosted on Docker Hub, not just Metabase, so you can try it with your containers. - Source: dev.to / almost 2 years ago
Try metabase.com its built with node and uses plugins. Source: about 2 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
OpenCV - OpenCV is the world's biggest computer vision library
Microsoft Power BI - BI visualization and reporting for desktop, web or mobile
NumPy - NumPy is the fundamental package for scientific computing with Python
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.