Based on our record, Scikit-learn should be more popular than Supermetrics. 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.
Supermetrics is a marketing data SaaS, they scaled internationally to more than $50M from Helsinki, Finland. Source: 12 months ago
Supermetrics | Senior Software Engineer | Full time | REMOTE (Portugal) | https://supermetrics.com/ Join our newly founded Internal Integrations Engineering team, develop scalable solutions to support our sales processes, and improve automation and internal tooling for Supermetrics' Sales, Finance, and Customer Support functions. We hope you have strong backend programming skills in PHP and Typescript/Javascript... - Source: Hacker News / about 1 year ago
Https://supermetrics.com is one of them but there are many more actually. Do a quick research about the alternative platforms and let me know if you need further help :). Source: over 1 year ago
Supermetrics powers 90% of our reporting, with automated report building so we can monitor our ad performance. - Source: dev.to / almost 3 years ago
The reality is that Apple will switch to ARM chips, and as devs, we need to be prepared. So this past weekend, faced with a shortage of Intel Macbook Pros for our new devs, we sat down to make it all work for our Supermetrics developers. - Source: dev.to / about 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 / 4 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 / 3 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
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