In this course you will be introduced to general concepts in machine learning, from supervised machine learning tasks to unsupervised tasks. On the first day you will gain a more in-depth knowledge of classification, one of the most widely used areas of machine learning, with a broad array of applications, including sentiment analysis, ad targeting, spam detection, medical diagnosis and image classification. On the second day you will investigate unsupervised learning, and clustering in more depth. Gain knowledge of major machine learning techniques through activities, interactive exercises, and hands-on crafting experience of learning models to solve real world problems.
- What types of different machine learning algorithms are used currently
- The differences between supervised learning and unsupervised learning
- What is machine learning and how is it used in different areas
- When to use and when not to use different learning methods
- Learning techniques and their characteristics
- Things to consider when building a learning model
- How to use machine learning to solve your problems
- Ways to evaluate a learning model
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