Implementing Machine Learning with R

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Course Overview

This three-day training will turn attendees into savvy data analysts with a solid foundation of unsupervised and supervised machine learning techniques. Students will use R to build data models and evaluate them. They will be able to find new patterns in data and predict behavior of new data points. The workshop optimizes learning by integrating practice time and discussion time in class to improve retention and provide individualized support throughout the session.

Implementing Machine Learning with R Course Includes:

3 days of instructor-led training
Class exercises in addition to training instruction
Courseware books, notepads, pens, highlighters and other materials
Course retake option
A second voucher included with full purchase
Full breakfast with variety of bagels, fruits, yogurt, doughnuts and juice
Tea, coffee, and soda available throughout the day
Freshly baked cookies every afternoon – *only at participating locations

Schedule

Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 301-258-8200.

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Course Outline

Introduction to foundational statistics:

Basic statistics
Expected value / standard deviation / variance / covariance
Statistical tests and significance
Linear regression
Single variable regression
Multiple regression

Best practices for model building:

Introduction to the model building process
Splitting data into train/test/validation sets
Multiple regression – dealing with correlated predictors
Commercial applications of regression

Regression model evaluation:

Distribution of errors: Q-Q plot, heteroscedasticity
Multiple regression
R2 and adjusted R2
p-values and t-test
F-test and F-distribution

Implementing clustering and unsupervised machine learning

What is unsupervised machine learning?
Introduction to clustering
k-means clustering on multi-dimensional data
Evaluating the quality of clustering Pitfalls of clustering

Introduction to classification:

k-Nearest Neighbors
Decision trees: gini coefficient and information gain
Introduction to random forests
Confusion matrices, misclassification rates
Base line errors and lift

Pitfalls and best practices of data science:

Understanding the limits of your data
Checking data validity
Ethical considerations
Best practices for data analysts