From the course: Amazon Web Services Machine Learning Essential Training
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Selecting algorithm for model training - Amazon Web Services (AWS) Tutorial
From the course: Amazon Web Services Machine Learning Essential Training
Selecting algorithm for model training
- [Instructor] Part of the power of SageMaker is the number of algorithms which Amazon has included. And a great way to explore them is to look at the samples. Many of the use the same example we had in our early movies of MNIST, the digits, and this is in Introduction to Amazon Algorithms. So you can see you've got, for example, you've got factorization_machines, you've got linear_learner, you've got xgboost. Now if you're new to this topic of algorithms, in addition to working with these notebooks, as foundational learning I would recommend that you work with the scikit-learn website. Now this is similar types of algorithms, but these are written really for university students and to run on laptops or desktop machines, but conceptually it can really help you, because as you can see, they're broken down to types of algorithms and you can compare types of algorithms, understand how they work. I've used it personally and it's really been helpful. So that we understand our algorithm…
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Contents
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Understanding ML platforms3m 53s
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Understanding and using AWS Machine Learning9m 15s
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Understanding SageMaker3m 54s
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Create Jupyter notebooks with SageMaker6m 12s
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Get data with SageMaker notebook6m 25s
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Train model with SageMaker job3m 6s
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Deploy and host model with SageMaker model2m 31s
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Use model from SageMaker endpoint4m 7s
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Selecting algorithm for model training5m 36s
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Advanced use of SageMaker2m 37s
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