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Microsoft Developer

Dev Intro to Data Science

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28 items
Last updated on Jul 9, 2020
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Introduction to the Developer's Intro to Data Science Video Series (1 of 28)
3:30
What is the Data Science Lifecycle? (2 of 28)
3:13
How do you define your business goal and scope your data science solution? (3 of 28)
2:09
What is Machine Learning? (4 of 28)
3:12
Which Machine Learning Algorithm Should You Use? (5 of 28)
3:07
What is AutoML? (6 of 28)
3:21
How do you create a machine learning resource in Azure? (7 of 28)
2:00
How do you setup your local environment for data exploration? (8 of 28)
4:13
How do Jupyter notebooks work in Visual Studio Code? (9 of 28)
2:35
Connect your Machine Learning resources to your local Visual Studio Code environment? (10 of 28)
3:05
How do you prepare your data for a time series forecast? (11 of 28)
2:46
Why do you split data into testing and training data in data science? (12 of 28)
2:27
What is an AutoML Config file? (13 of 28)
2:12
What should your parameters be when creating an AutoML Config file? (14 of 28)
1:27
How do you create an AutoML Config file & run your data science experiments on the cloud? (15 of 28)
3:32
What is Azure Machine Learning? (16 of 28)
3:00
How can you collaborate on Jupyter Notebooks using Azure Machine Learning studio? (17 of 28)
5:27
How do you choose the best model and perform feature engineering? (18 of 28)
1:59
How do you use Azure ML for best model selection and featurization? (19 of 28)
3:18
How do you evaluate and retrieve a time series forecast from Azure Machine Learning? (20 of 28)
4:07
How do you score your machine learning model on accuracy? (21 of 28)
3:38
How do you deploy a machine learning model as a web service within Azure? (22 of 28)
5:59
What have you learned from deploying a machine learning model as a web service? (23 of 28)
1:08
What is the importance of model deployment in machine learning? (24 of 28)
2:21
How do you select the right machine learning algorithm? (25 of 28)
3:46
How does ethics play a role in data science? (26 of 28)
5:18
Model interpretability & how can you incorporate it into your data science solutions? (27 of 28)
4:08
Concluding the Developer's Intro to Data Science Video Series (28 of 28)
0:43