Mathematica 11 Machine Learning
1h 16mIntermediate2017-04-21
Authors
Curt Frye
President of Technology and Society, Incorporated
Course details
Learn how to analyze data using the machine learning capabilities built into Mathematica 11. In this course, Curt Frye provides an overview of machine learning tasks, explains how to separate training data from test data, and shows how to import data from a file. He also demonstrates how to prepare data for machine learning, including how to replace values near zero with zero and sort elements using a rule. Curt also covers determining functions that generate data and performing supervised machine learning tasks.
Topics include:
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
Topics include:
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
Skills covered
MathematicaWolfram ResearchMachine LearningData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - Welcome
- 02 - What you should know
- 03 - Exercise files
1. Introducing Machine Learning
- 04 - Overview of machine learning tasks
- 05 - Separate training data from test data
- 06 - Import data from a file
2. Preparing Data for Machine Learning
- 07 - Standardize (normalize) or rescale data
- 08 - Replace values near zero with zero
- 09 - Interpolate data to enter missing values
- 10 - Count values by adherence or non-adherence to a rule
- 11 - Group elements using a rule
- 12 - Sort elements using a rule
3. Determining Functions that Generate Data
- 13 - Find a fit using a linear model
- 14 - Find a time series that fits given data
- 15 - Find a formula that represents a data set
- 16 - Find a function that generates a given sequence of values
4. Performing Supervised Learning Tasks
- 17 - Calculate the logistic sigmoid function for a data set
- 18 - Classify items using training data
- 19 - Predict values using training data
- 20 - Measure classifier function performance
- 21 - Measure predictor function performance
- 22 - Identify data clusters
Conclusion
- 23 - Next steps