Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ikivanc/azure-ml-resources
Compiled common Azure Machine Learning & Deep Learning resources new resources will be added
https://github.com/ikivanc/azure-ml-resources
Last synced: 5 days ago
JSON representation
Compiled common Azure Machine Learning & Deep Learning resources new resources will be added
- Host: GitHub
- URL: https://github.com/ikivanc/azure-ml-resources
- Owner: ikivanc
- Created: 2018-04-19T10:51:54.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-04-19T11:47:18.000Z (over 6 years ago)
- Last Synced: 2024-11-09T17:44:21.645Z (2 months ago)
- Homepage:
- Size: 2.93 KB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Azure ML/AI Resources
## Microsoft & Machine Learning / Artificial Intelligence Presentation
In this presentation you'll find Machine Learning / Deep Learning tools and services from Microsoft. Including Azure Machine Learning Workbench, Azure Notebooks, Azure Data Science Virtual Machines and more.
-
Microsoft & Machine Learning / Artificial Intelligence from İbrahim KIVANÇ
## Useful Python Courses
- Data manipulation experience with Python such as the Intro to Python for Data Science [Free Online Course](https://www.datacamp.com/courses/intro-to-python-for-data-science) and/or [numpy Info Here - Chapter 2](https://notebooks.azure.com/jakevdp/libraries/PythonDataScienceHandbook/tree/notebooks?page=2)
- Visualization experience with Python such as the Intermediate Python for Data Science [Free Online Course](https://www.datacamp.com/courses/intermediate-python-for-data-science) and/or [matplotlib Info Here - Chapter 4](https://notebooks.azure.com/jakevdp/libraries/PythonDataScienceHandbook/tree/notebooks?page=4)
- Jupyter notebook knowledge [Doc](http://jupyter-notebook.readthedocs.io/en/latest/examples/Notebook/Notebook%20Basics.html)
## Azure Machine Learning Workbench
- Install Azure Machine Learning Workbench [Ref](https://docs.microsoft.com/en-us/azure/machine-learning/preview/quickstart-installation)
- Tutorials for hands-on Experience
- [Prepare data](https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-classifying-iris-part-1)
- [Build models](https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-classifying-iris-part-2)
- [Deploy models](https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-classifying-iris-part-3)
- [Advanced data preparation](https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-bikeshare-dataprep)
- Real world Examples with Azure Machine Learning Workbench
- [Document collection analysis](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-document-collection-analysis)
- [Q & A matching](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-qna-matching)
- [Predictive maintenance](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-predictive-maintenance)
- [Aerial image classification](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-aerial-image-classification)
- [Server workload forecasting on terabytes of data](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-big-data)
- [Energy demand time series forecasting](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-time-series-forecasting)
- [Distributed tuning of hyperparameters](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-distributed-tuning-of-hyperparameters)
- [Customer churn prediction](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-churn-prediction)
- [Sentiment analysis with deep learning](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-sentiment-analysis-deep-learning)
- [Biomedical entity recognition - TDSP project](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-biomedical-recognition)
- [Classify US incomes - TDSP project](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-classifying-us-incomes)
- [Image classification using CNTK](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-image-classification-using-cntk)
- [Deep Learning for Predictive Maintenance](https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-deep-learning-for-predictive-maintenance)
## Data Manipulation
- Is your data ready for data science? [Doc](https://docs.microsoft.com/en-us/azure/machine-learning/studio/data-science-for-beginners-is-your-data-ready-for-data-science)
### Useful Packages
- matplotlib using pyplot dealing with figures (plotting) [Ref](https://matplotlib.org/2.0.2/users/pyplot_tutorial.html)
- numpy for image manipulation/processing/visualization [Ref](http://www.scipy-lectures.org/advanced/image_processing/)
## First Custom ML
- scikit-learn algorithm cheatsheet [Ref](http://scikit-learn.org/stable/index.html)
- Non-parametric and parametric algorithm differences [Ref](https://sebastianraschka.com/faq/docs/parametric_vs_nonparametric.html)
- scikit-learn Machine Learning guide with vocabulary [Ref](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#introduction)
- scikit-learn Supervised Learning [Ref](http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html)
- scikit-learn General User Guide [Ref](http://scikit-learn.org/stable/user_guide.html)
## Setting up your WorkSpace on Azure
Data Science Virtual Machine (DSVM)
- Introduction to the Azure Data Science Virtual Machine [Ref](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview)
- Create a Linux Data Science Virtual Machine (DSVM) and use JupyterHub to code with a team - [Video](https://www.youtube.com/watch?v=4b1G9pQC3KM) or [Doc](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough#jupyterhub)