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Based on our record, Scikit-learn should be more popular than 100 in 100 Challenge. 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.
Now that I've reached $1,000 MRR, my next big goal is to get 100 paying users in the next 100 days. Source: almost 2 years ago
If you're interested in joining the challenge the signup form and leaderboard are at https://100in100.co/. Source: almost 2 years ago
This month we're hosting a free group challenge for SaaS founders. The goal is simple: get 100 new paying users in 100 days. Source: almost 2 years ago
This last year we went from about 400 members to now over 2,000 (counting by active Slack members). A lot of the growth came from a challenge we ran called 100 users 100 days. The idea of the challenge was to get 100 new paying users in 100 days, with a leaderboard and weekly mastermind sessions for accountability. Definitely hope to run another round of the challenge soon. Source: over 2 years ago
- Try a public challenge like http://100in100.co. You'll get grouped with other entrepreneurs who are learning just like you. - Source: Hacker News / almost 3 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
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