This course has been a broad overview of machine learning. 0:00 We looked at a lot of big ideas and we even exported some code. 0:04 Let's do a quick review. 0:09 Machine Learning is a new approach to artificial intelligence. 0:11 With an emphasis on statistical analysis, and the ability for the computer to write 0:16 its own set of rules, rather than humans writing all of the conditional logic. 0:21 There are two major categories of Machine Learning, Supervised Learning and 0:27 Unsupervised Learning. 0:32 Supervised learning is when a machine intelligence predicts a category or 0:33 a quantity using models of classification and regression respectively. 0:38 Unsupervised learning is when a computer analyzes unlabeled data and 0:44 attempts to recognize patterns. 0:48 The most common unsupervised models use 0:51 clustering to group similar things together. 0:53 When handling data, remember that an example is a single element in a dataset. 0:57 And a feature is one characteristic of an example. 1:03 These basic models can lead to more complex and emerging behaviors in specific 1:07 domains like chatbots, image recognition, speech recognition and more. 1:12 More specialized applications like this can be served by higher level machine 1:19 learning platforms including AWS and IBM Watson. 1:23 If you didn't understand everything, don't worry. 1:29 That's normal. 1:32 I encourage you to watch this course again, explore the teacher notes, 1:33 and look at the documentation for scikit-learn. 1:37 You may also want to try exploring more problems using scikit-learn. 1:41 See if you can apply classification to another data set. 1:46 Or try something more advanced like regression or clustering. 1:50 With more practice and 1:55 repetition, your understanding will grow and you'll be ready for more. 1:56