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Machine Learning Foundations: Statistics

Offered By: LinkedIn Learning

Tags

Statistics & Probability Courses Data Analysis Courses Machine Learning Courses Linear Regression Courses Sampling Courses Summary Statistics Courses Probability Distributions Courses Correlation Courses

Course Description

Overview

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Learn how statistics can help you troubleshoot issues, optimize performance, and innovate, creating new machine learning models that are more efficient.

Syllabus

Introduction
  • Foundations of statistics for machine learning
  • What you should know
1. Introduction to Statistics
  • Defining statistics
  • Applications of statistics in ML
  • Types of data
2. The Summary Statistics
  • The mean
  • The median
  • The mode
  • The percentile
  • The percentage change
  • The range
  • The variance and the standard deviation
  • The standard error of the mean vs. the standard deviation
3. From Quantiles to Correlation
  • The quantiles and box plots
  • Missing data
  • The correlation
  • The covariance
  • The correlation coefficient
  • The correlation vs. causation
4. Random Variables and Probability Distribution
  • Introduction to probability distribution
  • The uniform distribution
  • The normal distribution
  • The Bernoulli distribution
  • The Multinoulli distribution
5. Sampling and Replacement
  • Selection with replacement
  • Selection without replacement
  • Bootstrapping
6. Linear Regression
  • Independent and dependent variables
  • Linear regression for continuous values
  • Fitting a line
  • Linear least squares
Conclusion
  • Next steps

Taught by

Terezija Semenski

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