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Based on our record, Scikit-learn should be more popular than KafkaHQ. It has been mentiond 28 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.
The project start as a side project (yet another side project I do the night and weekend) but was quickly promoted and used in a French Big Retail Company. This one trust on the project and decide to go production with Kestra. So they decide to inject some resource in order to develop some features that need and that is missing. But basically, not so much people for now. We are trying to start a community around... - Source: Hacker News / about 2 years ago
Hey HN, I'm really proud to share with you my new open source project: Kestra https://github.com/kestra-io/kestra I created a few years ago a successful open source AKHQ project: https://github.com/tchiotludo/akhq (renamed from KafkaHQ) which has been adopted by big companies like Best Buy, Pipedrive, BMW, Decathlon and many more. 2300 stars, 120 contributors, 10M docker downloads, much more than I expected. Now... - Source: Hacker News / about 2 years ago
Three years ago, I started another open source project, AKHQ, with the same license. Working with a successful project was an invaluable experience for me as I was able to learn how to build a community around a project. I've also learnt that an open source system won't pay the bills on its own. AKHQ required a lot of personal investment; Kestra has required a lot more and will continue to do so in the future!... - Source: dev.to / 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: 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
Kafka Manager - A tool for managing Apache Kafka.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
KafkaCenter - See what developers are saying about how they use KafkaCenter. Check out popular companies that use KafkaCenter and some tools that integrate with KafkaCenter.
OpenCV - OpenCV is the world's biggest computer vision library
rdkafka - The Apache Kafka C/C++ library. Contribute to edenhill/librdkafka development by creating an account on GitHub.
NumPy - NumPy is the fundamental package for scientific computing with Python