Join Curt Frye for an in-depth discussion in this video What you should know, part of Mathematica 11 Machine Learning.
- [Instructor] Before I get started with the main content of Mathematica 11 Machine Learning, I wanted to give a quick overview of what you should know to get the most out of this course. First, it'll help if you have basic knowledge of Mathematica 11 or earlier. The program, as well as the Wolfram Language, has some idiosyncrasies that you should know about. Second, it would help, but it's not necessary, for you to have experience doing data analysis. Machine Learning is an extension of basic statistical analysis, so the more you know of that and how to analyze data, the better off you'll be.
Third, one thing you should know is that when you open any of the notebooks, you should open the Evaluate menu and click Evaluate Notebook. That way, all of the data and variable assignments that I have created inside the script will be evaluated and actually assigned to that data. I mention that step specifically in every movie, but I wanted to mention it now because it is very important. Finally, you should have a willingness to experiment and explore. Mathematica 11 is an extremely powerful program and the new Machine Learning capabilities are also very powerful and flexible.
So, I hope that you take everything you learn from this course and use it as a base for further exploration. With all that in place, let's go ahead with the Mathematica 11 Machine Learning content.
- Separating training data from test data
- Importing data from a file
- Preparing data for machine learning
- Grouping and sorting elements using a rule
- Determining functions that generate data
- Finding a fit using a linear model
- Performing supervised learning tasks
- Classifying items using training data
- Identifying data clusters