1425 Open source projects that are alternatives of or similar to Benchm Ml

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.

RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)

A curated list of gradient boosting research papers with implementations.

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H2O.ai Machine Learning Interpretability Resources

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Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Distributed scikit-learn meta-estimators in PySpark

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

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Interactive and Reactive Data Science using Scala and Spark.

Sparkling Water provides H2O functionality inside Spark cluster

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

文本分类基准测试

Verteego Data Suite

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Efficient variant-call data storage and retrieval library using the TileDB storage library.

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Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS

A repository to keep track of all the code that I end up writing for my blog posts.

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Machine Learning University: Decision Trees and Ensemble Methods

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

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Example project implementing best practices for PySpark ETL jobs and applications.

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.

A simple Spark-powered ETL framework that just works 🍺

Tutorials and training material for the H2O Machine Learning Platform

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It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

⛈️ Rumble 1.11.0 "Banyan Tree"🌳 for Apache Spark | Run queries on your large-scale, messy JSON-like data (JSON, text, CSV, Parquet, ROOT, AVRO, SVM...) | No install required (just a jar to download) | Declarative Machine Learning and more

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Word2Vec models with Twitter data using Spark. Blog:

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