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# Mathematica 11 Machine Learning

## Overview

Get started with machine learning. Learn how to separate training data from test data, prepare data for machine learning, perform supervised machine learning tasks, and more.

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.

## Syllabus

Introduction
• Welcome
• What you should know
• Exercise files
1. Introducing Machine Learning
• Overview of machine learning tasks
• Separate training data from test data
• Import data from a file
2. Preparing Data for Machine Learning
• Standardize (normalize) or rescale data
• Replace values near zero with zero
• Interpolate data to enter missing values
• Count values by adherence or non-adherence to a rule
• Group elements using a rule
• Sort elements using a rule
3. Determining Functions that Generate Data
• Find a fit using a linear model
• Find a time series that fits given data
• Find a formula that represents a data set
• Find a function that generates a given sequence of values
4. Performing Supervised Learning Tasks
• Calculate the logistic sigmoid function for a data set
• Classify items using training data
• Predict values using training data
• Measure classifier function performance
• Measure predictor function performance
• Identify data clusters
Conclusion
• Next steps

Curt Frye

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