Künstliche Intelligenz und Maschinelles Lernen in der Praxis

1. 00:00Welcome to the overview of projects.
2. 00:03Use for the first scenario like a data set from California.
3. 00:08In this record, information about individual residential areas, such as
4. 00:13the number of houses or the proximity to the beach.
5. 00:16The goal for us is a Supervised Learning model , which is based on historical data
6. 00:23the median of a house in a specific area.
7. 00:28The data are stored in a table structure, similar to Excel.
8. 00:33As a machine learning method we use the regression here, which is a
9. 00:38Supervised Learning Technique.
10. 00:41In the second week we show how man with machine learning a suggestion system for
11. 00:45can create new movies.
12. 00:47As a data base we use a Data set with general information around
13. 00:53the films and the respective Evaluation of individual persons.
14. 00:58Here too we find the data in a sort of tabular form.
15. 01:01We use clustering as a method and other unsupervised techniques.
16. 01:07Then we deal in the third Week with the distinction of positive
17. 01:12and negative statements for an evaluation platform.
18. 01:17As a data base we use Comments from the respective evaluation platform
19. 01:22with one label each, whether a Comment is positive or negative.
20. 01:28Here we find the data in Text form again and try a classification
21. 01:33in the event of a conflict. So a Supervised Learning Technique.
22. 01:38In the fourth week we will devote ourselves to the Recognition of sign language from images.
23. 01:44We want a data set with Pictures, with labels, in the alphabet from A to Z
24. 01:49classification.
25. 01:52In the course we will each briefly, theoretical units and practical
26. 01:57Show examples.
27. 02:00The practical examples we will during the course so that they
28. 02:04and click through something so you can try around.
29. 02:09After this overview of the projects in the course
30. 02:12we will next with the overview of the content in week 1.

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