benchm-ml - A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc

•    R

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This project aims at a minimal benchmark for scalability, speed and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications. Note: While a large part of this benchmark was done in Spring 2015 reflecting the state of ML implementations at that time, this repo is being updated if I see significant changes in implementations or new implementations have become widely available (e.g. lightgbm). Also, please find a summary of the progress and learnings from this benchmark at the end of this repo.

machine-learning data-science r gradient-boosting-machine random-forest deep-learning xgboost h2o spark

tgboost - Tiny Gradient Boosting Tree

•    Java

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It is a Tiny implement of Gradient Boosting tree, based on XGBoost's scoring function and SLIQ's efficient tree building algorithm. TGBoost build the tree in a level-wise way as in SLIQ (by constructing Attribute list and Class list). Currently, TGBoost support parallel learning on single machine, the speed and memory consumption are comparable to XGBoost. Handle missing value, XGBoost learn a direction for those with missing value, the direction is left or right. TGBoost take a different approach: it enumerate missing value go to left child, right child and missing value child, then choose the best one. So TGBoost use Ternary Tree.

boosted-trees gradient-boosting-machine machine-learning xgboost sliq

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

•    Python

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more

•    C++

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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.

gbdt gbrt gbm distributed-systems xgboost gradient-boosting histogram

tpot - A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming

•    Python

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Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.

machine-learning data-science automl automation scikit-learn hyperparameter-optimization model-selection parameter-tuning automated-machine-learning random-forest gradient-boosting feature-engineering xgboost genetic-programming

auto_ml - Automated machine learning for analytics & production

•    Python

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auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. All of these projects are ready for production. These projects all have prediction time in the 1 millisecond range for a single prediction, and are able to be serialized to disk and loaded into a new environment after training.

machine-learning data-science automated-machine-learning gradient-boosting scikit-learn machine-learning-pipelines machine-learning-library production-ready automl lightgbm analytics feature-engineering hyperparameter-optimization deep-learning xgboost keras deeplearning tensorflow artificial-intelligence

Machine-Learning-with-Python - Python codes for common Machine Learning Algorithms

•    Jupyter

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Python codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithm

elasticsearch-learning-to-rank - Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch

•    Java

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Rank Elasticsearch results using tree based (LambdaMART, Random Forest, MART) and linear models. Models are trained using the scores of Elasicsearch queries as features. You train offline using tooling such as with xgboost or ranklib. You then POST your model to a to Elasticsearch in a specific text format (the custom "ranklib" language, documented here). You apply a model using this plugin's ltr query. See blog post and the full demo (training and searching).Models are stored using an Elasticsearch script plugin. Tree-based models can be large. So we recommend increasing the script.max_size_in_bytes setting. Don't worry, just because tree-based models are verbose, doesn't nescesarilly imply they'll be slow.

elasticsearch relevant-search machine-learning search-relevance

XLearning - AI on Hadoop

•    Java

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XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.

hadoop tensorflow caffe mxnet yarn

xgboost-doc-zh - XGBoost 中文文档

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XGBoost 中文文档

open-solution-home-credit - Open solution to the Home Credit Default Risk challenge :house_with_garden:

•    Python

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This is an open solution to the Home Credit Default Risk challenge 🏡. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.

machine-learning deep-learning kaggle pipeline feature-engineering reproducible-experiments reproducibility pipeline-framework lightgbm xgboost neptune competition credit-scoring credit-risk open-source python3 python35

kubeflow - Machine Learning Toolkit for Kubernetes

•    Go

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The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

ml kubernetes minikube tensorflow notebook jupyterhub google-kubernetes-engine machine-learning

coremltools - Converter tools for Core ML.

•    Python

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Core ML community tools contains all supporting tools for CoreML model conversion and validation. This includes Scikit Learn, LIBSVM, Caffe, Keras and XGBoost. We recommend using virtualenv to use, install, or build coremltools. Be sure to install virtualenv using your system pip.

Machine-Learning-Links-And-Lessons-Learned - List of all the lessons learned, best practices, and links from my time studying machine learning

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List of all the lessons learned, best practices, and links from my time studying machine learning. "How do you get started with machine learning?". With AI and ML becoming such huge words in the tech industry, it's hard to go a full week without hearing something along these lines on online forums, in discussions with other students at UCLA, and even from fellow pre-meds and humanities majors. From my own experience of getting familiar with ML and from my experiences of teaching others through ACM AI, here's my best response to that question.

machine-learning

ml-agents - Unity Machine Learning Agents

•    CSharp

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Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

reinforcement-learning unity3d deep-learning unity deep-reinforcement-learning neural-networks

handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

•    Jupyter

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First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

tensorflow scikit-learn machine-learning deep-learning neural-network ml distributed jupyter-notebook

machine-learning-samples - Sample applications built using AWS' Amazon Machine Learning.

•    Python

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Each subdirectory contains sample code for using Amazon Machine Learning. Refer to the README.md file in each sub-directory for details on using each sample.This sample application shows how to use Amazon Mechanical Turk to create a labeled dataset from raw tweets, and then build a machine learning model using the Amazon Machine Learning API that predicts whether or not new tweets should be acted upon by customer service. The sample shows how to set up an automated filter using AWS Lambda that monitors tweets on an Amazon Kinesis stream and sends notifications whenever the ML Model predicts that a new tweet is actionable. Notifications go to Amazon SNS, allowing delivery to email, SMS text messages, or other software services.

machine-learning-samples - Sample applications built using AWS' Amazon Machine Learning.

•    Python

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Each subdirectory contains sample code for using Amazon Machine Learning. Refer to the README.md file in each sub-directory for details on using each sample. This sample application shows how to use Amazon Mechanical Turk to create a labeled dataset from raw tweets, and then build a machine learning model using the Amazon Machine Learning API that predicts whether or not new tweets should be acted upon by customer service. The sample shows how to set up an automated filter using AWS Lambda that monitors tweets on an Amazon Kinesis stream and sends notifications whenever the ML Model predicts that a new tweet is actionable. Notifications go to Amazon SNS, allowing delivery to email, SMS text messages, or other software services.

Awesome-CoreML-Models - Largest list of models for Core ML (for iOS 11+)

•    HTML

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We've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.

coreml coreml-model apple machine-learning curated-list coreml-framework coreml-models coremltools awesome-list models model download awesome core-ml ml caffe caffemodel tensorflow-models ios ios11

ml - Machine learning tools in JavaScript

•    Javascript

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This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. It will be available as the global ML variable. The package is in UMD format and can be "required" within webpack or requireJS.

machine-learning ml machine learning data mining datamining