Machine Learning Foundations: Statistics

Machine Learning Foundations: Statistics

1h 20mBeginner2025-06-09

Authors

Terezija Semenski

Terezija Semenski

Software Developer, Mathematician, Writer, and Learner

Course details

Machine learning models have revolutionized how we work, across a multitude of industries. But going deeper with ML models and actually understanding how they work will allow you to optimize performance, innovate, troubleshoot issues, and create new and more efficient ML models. In this course, the fourth part of the Machine Learning Foundations series, Terezija Semenski explains how a deep understanding of statistics can help you excel when it comes to machine learning projects. Terezija shows how statistics plays a large role in machine learning —beyond just crunching numbers—and shows you how to use statistics to gain insights into the data, understand the uncertainties associated with predictions, and make data-driven decisions with confidence.

Skills covered

StatisticsMachine LearningPythonArtificial Intelligence (AI)Data ScienceOpen SourceOne-Off

Concepts

Introduction

  • Foundations of statistics for machine learning
  • What you should know

Introduction to Statistics

  • Defining statistics
  • Applications of statistics in ML
  • Types of data

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

From Quantiles to Correlation

  • The quantiles and box plots
  • Missing data
  • The correlation
  • The covariance
  • The correlation coefficient
  • The correlation vs. causation

Random Variables and Probability Distribution

  • Introduction to probability distribution
  • The uniform distribution
  • The normal distribution
  • The Bernoulli distribution
  • The Multinoulli distribution

Sampling and Replacement

  • Selection with replacement
  • Selection without replacement
  • Bootstrapping

Linear Regression

  • Independent and dependent variables
  • Linear regression for continuous values
  • Fitting a line
  • Linear least squares

Conclusion

  • Next steps
40,000 Toman