| 1. Agenda.mp4 | 8.7 MB | ||
| 1. Agenda.srt | 2 KB | ||
| 1. Azure AutoML.mp4 | 34.2 MB | ||
| 1. Azure AutoML.srt | 7 KB | ||
| 1. AzureML Architecture-Part1.mp4 | 7.5 MB | ||
| 1. AzureML Architecture-Part1.srt | 1.7 KB | ||
| 1. AzureML Pipeline-Part1.mp4 | 13.7 MB | ||
| 1. AzureML Pipeline-Part1.srt | 2.8 KB | ||
| 1. Create Workspace.mp4 | 37 MB | ||
| 1. Create Workspace.srt | 8.8 KB | ||
| 1. Create and execute ML in local Virtual environment-Part1.mp4 | 15.2 MB | ||
| 1. Create and execute ML in local Virtual environment-Part1.srt | 6.3 KB | ||
| 1. Creating YML file to create virtual environment.mp4 | 12.7 MB | ||
| 1. Creating YML file to create virtual environment.srt | 3.9 KB | ||
| 1. Importing Python AzureML Libraries.mp4 | 13.2 MB | ||
| 1. Importing Python AzureML Libraries.srt | 3.5 KB | ||
| 1. Register azure SQLDB to AzureML-Part1.mp4 | 21.6 MB | ||
| 1. Register azure SQLDB to AzureML-Part1.srt | 3.1 KB | ||
| 1. Train Model-Part1.mp4 | 67 MB | ||
| 1. Train Model-Part1.srt | 12.1 KB | ||
| 10. Deploy Model Azure ML-Part4.mp4 | 49.1 MB | ||
| 10. Deploy Model Azure ML-Part4.srt | 9.4 KB | ||
| 11. ScorePredict the Model in AzureML.mp4 | 57.5 MB | ||
| 11. ScorePredict the Model in AzureML.srt | 7.2 KB | ||
| 2. AzureML Architecture-Part2.mp4 | 26.8 MB | ||
| 2. AzureML Architecture-Part2.srt | 6.8 KB | ||
| 2. AzureML Pipeline-Part2.mp4 | 51.3 MB | ||
| 2. AzureML Pipeline-Part2.srt | 9.1 KB | ||
| 2. Create and execute ML in local Virtual environment-Part2.mp4 | 28.8 MB | ||
| 2. Create and execute ML in local Virtual environment-Part2.srt | 7.6 KB | ||
| 2. Introduction of Azure Machine learning.mp4 | 1.8 MB | ||
| 2. Introduction of Azure Machine learning.srt | 716.8 B | ||
| 2. Pytorch Deep Learning Train Model.mp4 | 47.8 MB | ||
| 2. Pytorch Deep Learning Train Model.srt | 9.4 KB | ||
| 2. Register azure SQLDB to AzureML-Part2.mp4 | 56.1 MB | ||
| 2. Register azure SQLDB to AzureML-Part2.srt | 9.2 KB | ||
| 2. Train Model-Part2.mp4 | 49.4 MB | ||
| 2. Train Model-Part2.srt | 9.5 KB | ||
| 2. Workspace Capabilities.mp4 | 38.9 MB | ||
| 2. Workspace Capabilities.srt | 8.4 KB | ||
| 2. Workspace creation using Python SDK.mp4 | 31.2 MB | ||
| 2. Workspace creation using Python SDK.srt | 5.5 KB | ||
| 3. AzureML Architecture-Part3.mp4 | 21.8 MB | ||
| 3. AzureML Architecture-Part3.srt | 10.2 KB | ||
| 3. AzureML Pipeline-Part3.mp4 | 68.1 MB | ||
| 3. AzureML Pipeline-Part3.srt | 11.6 KB | ||
| 3. Create Azure Free Account.mp4 | 12.1 MB | ||
| 3. Create Azure Free Account.srt | 3.2 KB | ||
| 3. Explore Workspace in Azure Portal.mp4 | 9 MB | ||
| 3. Explore Workspace in Azure Portal.mp4.jpg?042148 | 159.1 KB | ||
| 3. Explore Workspace in Azure Portal.srt | 1.9 KB | ||
| 3. Learn to pass argument at runtime in Python.mp4 | 43.3 MB | ||
| 3. Learn to pass argument at runtime in Python.srt | 8.2 KB | ||
| 3. Machine Learning in Local environment.mp4 | 18.9 MB | ||
| 3. Machine Learning in Local environment.srt | 4.9 KB | ||
| 3. Register azure SQLDB to AzureML-Part3.mp4 | 51.4 MB | ||
| 3. Register azure SQLDB to AzureML-Part3.srt | 9.9 KB | ||
| 3. Train Model.mp4 | 8.3 MB | ||
| 3. Train Model.srt | 1.8 KB | ||
| 4. Azure compute cluster creation using Python.mp4 | 41.5 MB | ||
| 4. Azure compute cluster creation using Python.srt | 6.3 KB | ||
| 4. Pytorch Train Model in AzureML using Python SDK.mp4 | 21.7 MB | ||
| 4. Pytorch Train Model in AzureML using Python SDK.srt | 5.2 KB | ||
| 4. Runtime argument in AzureML.mp4 | 23.8 MB | ||
| 4. Runtime argument in AzureML.srt | 3.4 KB | ||
| 4. Schedule AzureML Pipeline.mp4 | 13.5 MB | ||
| 4. Schedule AzureML Pipeline.srt | 2.5 KB | ||
| 5. Log the metrices.mp4 | 33.3 MB | ||
| 5. Log the metrices.srt | 5.3 KB | ||
| 6. Register the Model.mp4 | 23.6 MB | ||
| 6. Register the Model.srt | 4.3 KB | ||
| 7. Deploy Model in Azure ML-Part1.mp4 | 23 MB | ||
| 7. Deploy Model in Azure ML-Part1.srt | 5.1 KB | ||
| 8. Deploy Model Azure ML-Part2.mp4 | 10.6 MB | ||
| 8. Deploy Model Azure ML-Part2.srt | 3 KB | ||
| 9. Deploy Model Azure ML-Part3.mp4 | 36.9 MB | ||
| 9. Deploy Model Azure ML-Part3.srt | 6.5 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Demo.ipynb | 143.5 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| datapreppipe.py | 512 B | ||
| diabetes.csv | 22.9 KB | ||
| dlmodel.py | 2.5 KB | ||
| envfile.yml | 102.4 B | ||
| score.py | 614.4 B | ||
| test.csv | 21.8 KB | ||
| train.py | 1.1 KB | ||
| trainflowpipe.py | 1 KB | ||
| ▲ 88 total files | |||
DP-100 Azure Machine Learning in Python-Basic to Advance
Last Update: 7/2021
Duration: 3h 24m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.1 GB
Genre: eLearning | Language: English
Machine learning workflow orchestration to automate model training, deployment, and management processes using python.
What you'll learn:
Azure ML Architecture
Train a Machine learning with Azure ML in python
Attached Storage with your Azure ML
Deploy real time machine service
Azure Auto ML
Azure Designer Studio
Requirements:
Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification
Python Basic knowledge
Description:
This course has been designed keeping in mind entry level Data Scientists or no background in programming. This course will also help the data scientists and python developers to learn the AzureML .
This course is designed based on latest changes done in DP-100 Certification.
This course would also be useful for the experts who needs to know how to create and deploy a machine learning environment in production. Environment could be cloud or on-premises
In this course student will learn to:-
Create Free Azure account and how to access AzureML studio
We will learn complete architecture of Azure Machine learning
How we can create a virtual env for python using yml file in your local machine
Will train machine learning and deep learning algorithm in azure ml in local machine and same code will be executed in azure as well.
How different storage can be attached to azure ml like Azure SQL DB
How to deploy ML in azure and do real time prediction
We will cover how to create machine learning pipeline and how to do hyperparameter tuning
We will also learn how to implement Azure Auto ML
How to create complete machine learning lifecycle without writing a single code with the help of azure machine learning studio.
Understanding of the components and how they work together to assist in the process of building, deploying, and maintaining machine learning models.
Provision an Azure Machine Learning workspace
Use tools and interfaces to work with Azure Machine Learning.
Run code-based experiments in an Azure Machine Learning workspace.
Who this course is for:
Developer or data scientist execute model in Azure
Developers who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning
Business Analysts who want to apply Data Science to solve business problems
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| torrent name | size | uploader | age | seed | leech |
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Designing and Implementing a Data Science Solution on Azure (DP-100) - Train and Deploy Models Posted by
freecoursewb in Other
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275.8 MB | freecoursewb | 7 months | 1 | 0 |
| 7.4 GB | tutsnode | 5 years | 3 | 1 |
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