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Finding Donors: Build an algorithm that best identifies potential donors.
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Image Classifier: Implement an image classifier with PyTorch.
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Identify Customer Segment: Apply unsupervised learning techniques to identify clusters of the population that are most likely to be purchasers of products for a mailout campaign.
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Write a Data Science Blog Post: Analyze Airbnb data in Boston and Seattle to find out what kind of listings are more likely to have higher revenues. Accompanying blog post can be found here
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Build Pipelines to Classify Messages with Figure Eight: Build ETL and ML pipelines to classify messages sent during disasters.
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Design a Recommendation Engine with IBM: Implement recommendation techniques using data from the IBM Watson Studio platform.
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Data Science Capstone Project. Explore data from Starbucks Rewards Mobile App and implement a promotional strategy with uplift models. Accompanying blog post can be found here: part 1, part 2.
These are optional, ungraded exercises for the course
Starbucks Portfolio Exercise: Implement uplift models to identify which customers we should send promotions to entice them to purchase a product. Accompanying blog post can be found here