zero-to-mastery-ml VS ydata-profiling

Compare zero-to-mastery-ml vs ydata-profiling and see what are their differences.

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zero-to-mastery-ml ydata-profiling
126 43
2,581 12,053
- 0.9%
8.3 8.5
6 days ago 8 days ago
Jupyter Notebook Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

zero-to-mastery-ml

Posts with mentions or reviews of zero-to-mastery-ml. We have used some of these posts to build our list of alternatives and similar projects.

ydata-profiling

Posts with mentions or reviews of ydata-profiling. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-26.

What are some alternatives?

When comparing zero-to-mastery-ml and ydata-profiling you can also consider the following projects:

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

dtale - Visualizer for pandas data structures

machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.

DataProfiler - What's in your data? Extract schema, statistics and entities from datasets

probability - Probabilistic reasoning and statistical analysis in TensorFlow

dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

fastbook - The fastai book, published as Jupyter Notebooks

lux - Automatically visualize your pandas dataframe via a single print! 📊 💡

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.

evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b

dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.