Diffgram VS Scikit-learn

Compare Diffgram VS Scikit-learn and see what are their differences

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Scikit-learn

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

Landing page //
2021-04-22

Diffgram is open source annotation and training data software.

Flexible deploy and many integrations - run Diffgram anywhere in the way you want.
Scale every aspect - from volume of data, to number of supervisors, to ML speed up approaches.
Fully featured - 'batteries included'.

Landing page //
2022-05-06

Easily Import & Export from {AWS, GCP} without API integration

Scikit-learn videos

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Learning Scikit-Learn (AI Adventures)

Category Popularity

0-100% (relative to Diffgram and Scikit-learn)

Scikit-learn

Data Science And Machine Learning

7 7%

Data Science And Machine Learning

93% 93

100 100%

0% 0

Data Science Tools

0 0%

Data Science Tools

100% 100

Data Labeling

100 100%

Data Labeling

0% 0

User comments

Share your experience with using Diffgram and Scikit-learn. 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 Diffgram and Scikit-learn

Diffgram Reviews

Sharon

manager at Mcormicki | about 3 years ago

Fast and did everything we needed
Overall really really happy with the tool and the team. Excited that it's now open source our team is already building an integration

🏁 Competitors: Labelbox

👍 Pros: Fast|Powerful|Flexible
saashub-capital

Founder at Capital | over 3 years ago

Best data handling - fast response times
Amazing import options and data sync. Really happy with speed and responsiveness of team.

🏁 Competitors: Labelbox

👍 Pros: Data|Interface|Speed|Support response time

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

Social recommendations and mentions

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

Diffgram mentions (0)

We have not tracked any mentions of Diffgram yet. Tracking of Diffgram recommendations started around Mar 2021.

Scikit-learn mentions (28)

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 / 2 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 / 11 months 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: 12 months 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: 12 months ago
Help on using R for Machine Learning?
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