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IT: Programming & Software Development
Statistical Inference and ML in R
Curriculum
1 Section
26 Lessons
30 hours
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Unit 2 — Statistical Inference and ML in R
26
1.1
Standard Scores in R
20 mins
1.2
The High and the Low
20 mins
1.3
Working with Normal Distributions
20 mins
1.4
An EXTREMELY Important Idea: The Central Limit Theorem
20 mins
1.5
Hypothesis Tests and Sampling Distributions
20 mins
1.6
Sampling Distributions Revisited
20 mins
1.7
Introducing the Rattle package
20 mins
1.8
Decision Trees in R
20 mins
1.9
Random Forests in R
20 mins
1.10
Separability: It’s Usually Nonlinear
20 mins
1.11
K-Means Clustering in R
20 mins
1.12
Artificial Neural Networks
20 mins
1.13
Enter Machine Learning
20 mins
1.14
Warming Up
20 mins
1.15
Creating Your First shiny Project
20 mins
1.16
Exploring Dashboard Layouts
20 mins
1.17
Getting RStudio
20 mins
1.18
R Formulas
20 mins
1.19
Kicking It Up a Notch to ggplot2
20 mins
1.20
The Average in R: mean()
20 mins
1.21
Standard Deviation in R
20 mins
1.22
Where Do You Stand?
20 mins
1.23
Living in the Moments
20 mins
1.24
Meeting a Distinguished Member of the Family
20 mins
1.25
Confidence: It Has Its Limits!
20 mins
1.26
Catching Some Z’s Again
20 mins
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