- In this part of the course, we're going to cover machine learning. Machine learning refers to the process by which machines improve their performance without explicit programming. With machine learning, machines discover patterns, are able to make predictions, and get better over time with exposure to data. There are a couple of reasons why machine learning is important. For one thing, designers can't anticipate all possible situations a program or agent might face. For another, sometimes we have no idea how to program a solution.
For instance, we don't really know how we recognize faces. For these reasons, it's really valuable that machines can learn solutions on their own. In this section, we'll look at three main types of machine learning. Supervised learning, unsupervised learning, and reinforcement learning. For each type of machine learning, we'll provide an example, we'll describe how it works and we'll let you know what it's good for, what some of the key applications are. We'll also briefly discuss some of the key machine learning algorithms and models such as artificial neural networks, support vector machines and ensemble learning.
- Artificial intelligence explained
- Cognitive technologies explained
- Supervised, unsupervised, and reinforcement learning
- Machine learning models and algorithms
- Language, speech, and visual processing
- Business applications of cognitive tech
- The impact of cognitive technologies at work
- Future of cognitive technologies