Scikit-learn VS Secret Escapes

Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Secret Escapes

Hand-picked hotels, members-only rates.

Landing page //
2022-05-06

Landing page //
2019-07-15

Learning Scikit-Learn (AI Adventures)

Secret Escapes videos

+ Add

Secret Escapes Review 2020 - You've Seen The Ad Now For The Secret Escapes Review!

Category Popularity

0-100% (relative to Scikit-learn and Secret Escapes)

Secret Escapes

Data Science And Machine Learning

100 100%

Data Science And Machine Learning

0% 0

Travel

0 0%

Travel

100% 100

Data Science Tools

100 100%

Data Science Tools

0% 0

API Tools

0 0%

API Tools

100% 100

User comments

Share your experience with using Scikit-learn and Secret Escapes. For example, how are they different and which one is better?

Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Secret Escapes

Scikit-learn Reviews

15 data science tools to consider using in 2021

Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Source: searchbusinessanalytics.techtarget.com

Secret Escapes Reviews

We have no reviews of Secret Escapes yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. 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.

Scikit-learn mentions (29)

Essential Deep Learning Checklist: Best Practices Unveiled
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 / 13 days ago
How to Build a Logistic Regression Model: A Spam-filter Tutorial
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
Link Prediction With node2vec in Physics Collaboration Network
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
WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
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
PSA: You don't need fancy stuff to do good work.
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