6852 Open source projects that are alternatives of or similar to Interpretable_machine_learning_with_python

H2O.ai Machine Learning Interpretability Resources

A curated list of awesome machine learning interpretability resources.

Materials for GWU DNSC 6279 and DNSC 6290.

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018

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Tutorials and training material for the H2O Machine Learning Platform

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Machine learning fundamentals lesson in interactive notebooks

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.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

The "Python Machine Learning (1st edition)" book code repository and info resource

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Human-explainable AI.

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Public repository for the PM4Py (Process Mining for Python) project.

Probabilistic reasoning and statistical analysis in TensorFlow

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Debugging, monitoring and visualization for Python Machine Learning and Data Science

experiments with python

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

Interactive reports to analyze machine learning models during validation or production monitoring.

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

A curated list of fraud detection papers using graph information or graph neural networks

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阿水的数据竞赛开源分支

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Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

A curated list of research, applications and projects built using the H2O Machine Learning platform

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