These Jupyter notebooks were based on a course offered by the University of Washington.
Following this material, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the material you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.
- Identify potential applications of machine learning in practice.
- Describe the core differences in analyses enabled by regression, classification, and clustering.
- Select the appropriate machine learning task for a potential application.
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
- Represent your data as features to serve as input to machine learning models.
- Assess the model quality in terms of relevant error metrics for each task.
- Utilize a dataset to fit a model to analyze new data.
- Build an end-to-end application that uses machine learning at its core.
- Implement these techniques in Python.