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Live image classification
Training and tuning Core ML models
Using custom models with Vision
Augmenting data sets with Create ML
Data curation for optimal training
Creating Python environments for ML using Anaconda
Using the Turi Create API in Jupyter Notebook

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Welcome to the Machine Learning in iOS video course! In this introduction, find out what the course will cover.

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Apple's Vision framework was introduced along with Core ML in iOS 11. They integrate easily for computer vision tasks.

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Analyze the results of using a binary classifier. It will attempt to classify any image you show it, even if that results in nonsense!

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Using other people’s models is often not sufficient. Create ML will allow you to train your own! Here you'll use it in a playground.

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Collecting good data is key in ML. Hear about the curation process our team went through to provide the dataset for this course.

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Reiterate the ways you've learned to work with ML models, whatever your budget for data collection and model training might be.

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Get set up with a Python-based machine learning environment, based around Anaconda and Jupyter Notebook.

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Despite the difference in programming languages (Python vs. Swift), Turi Create shares a lot with Create ML – including transfer learning.

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Continue training your snacks classifier using Turi Create, in order to learn more about its API for managing models.

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Turi Create's public API offers limited control over the model training process, but it's open source and hides some treasures!

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You've gotten familiar with machine learning in two popular environments. But, armed with your Python knowledge, there's still a huge world of ML to explore!

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