From 6627cf20035f94f554782d37fa85f0a9d92d9b17 Mon Sep 17 00:00:00 2001 From: Najeeb Kazmi Date: Thu, 17 Oct 2019 13:45:22 -0700 Subject: [PATCH 1/2] Add info to README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1ec683ab..78568895 100644 --- a/README.md +++ b/README.md @@ -2,9 +2,13 @@ `nimbusml` is a Python module that provides Python bindings for [ML.NET](https://github.com/dotnet/machinelearning). -ML.NET was originally developed in Microsoft Research and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and others. `nimbusml` was built to enable data science teams that are more familiar with Python to take advantage of ML.NET's functionality and performance. +ML.NET was originally developed in Microsoft Research and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel, and others. `nimbusml` was built to enable data science teams that are more familiar with Python to take advantage of ML.NET's functionality and performance. -This package enables training ML.NET pipelines or integrating ML.NET components directly into [scikit-learn](https://scikit-learn.org/stable/) pipelines (it supports `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs). +`nimbusml` enables training ML.NET pipelines or integrating ML.NET components directly into [scikit-learn](https://scikit-learn.org/stable/) pipelines. It adheres to existing `scikit-learn` conventions, allowing simple interoperability between `nimbusml` and `scikit-learn` components, while adding a suite of fast, highly optimized, and scalable algorithms, transforms, and components written in C++ and C\#. + +See examples below showing interoperability with `scikit-learn`. A more detailed example in the [documentation](https://docs.microsoft.com/en-us/nimbusml/tutorials/b_c-sentiment-analysis-3-combining-nimbusml-and-scikit-learn) shows using a `nimbusml` component in a `scikit-learn` pipeline, and creating pipeline using only `nimbusml` components. + +`nimbusml` supports `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs. In addition, `nimbusml` also supports streaming from files without loading the dataset into memory with `FileDataStream`, which allows training on data significantly exceeding memory. Documentation can be found [here](https://docs.microsoft.com/en-us/NimbusML/overview) and additional notebook samples can be found [here](https://github.com/Microsoft/NimbusML-Samples). From f303da53c050ef70688d87863c7fa7765cb2df4f Mon Sep 17 00:00:00 2001 From: Najeeb Kazmi Date: Thu, 17 Oct 2019 13:48:12 -0700 Subject: [PATCH 2/2] update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 78568895..2f4207a5 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ML.NET was originally developed in Microsoft Research and is used across many pr `nimbusml` enables training ML.NET pipelines or integrating ML.NET components directly into [scikit-learn](https://scikit-learn.org/stable/) pipelines. It adheres to existing `scikit-learn` conventions, allowing simple interoperability between `nimbusml` and `scikit-learn` components, while adding a suite of fast, highly optimized, and scalable algorithms, transforms, and components written in C++ and C\#. -See examples below showing interoperability with `scikit-learn`. A more detailed example in the [documentation](https://docs.microsoft.com/en-us/nimbusml/tutorials/b_c-sentiment-analysis-3-combining-nimbusml-and-scikit-learn) shows using a `nimbusml` component in a `scikit-learn` pipeline, and creating pipeline using only `nimbusml` components. +See examples below showing interoperability with `scikit-learn`. A more detailed example in the [documentation](https://docs.microsoft.com/en-us/nimbusml/tutorials/b_c-sentiment-analysis-3-combining-nimbusml-and-scikit-learn) shows how to use a `nimbusml` component in a `scikit-learn` pipeline, and create a pipeline using only `nimbusml` components. `nimbusml` supports `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs. In addition, `nimbusml` also supports streaming from files without loading the dataset into memory with `FileDataStream`, which allows training on data significantly exceeding memory.