Machine Learning Foundations: Statistics
1h 20mBeginner2025-06-09
Authors

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
0. Introduction
- 01 - Foundations of statistics for machine learning
- 02 - What you should know
1. Introduction to Statistics
- 03 - Defining statistics
- 04 - Applications of statistics in ML
- 05 - Types of data
2. The Summary Statistics
- 06 - The mean
- 07 - The median
- 08 - The mode
- 09 - The percentile
- 10 - The percentage change
- 11 - The range
- 12 - The variance and the standard deviation
- 13 - The standard error of the mean vs. the standard deviation
3. From Quantiles to Correlation
- 14 - The quantiles and box plots
- 15 - Missing data
- 16 - The correlation
- 17 - The covariance
- 18 - The correlation coefficient
- 19 - The correlation vs. causation
4. Random Variables and Probability Distribution
- 20 - Introduction to probability distribution
- 21 - The uniform distribution
- 22 - The normal distribution
- 23 - The Bernoulli distribution
- 24 - The Multinoulli distribution
5. Sampling and Replacement
- 25 - Selection with replacement
- 26 - Selection without replacement
- 27 - Bootstrapping
6. Linear Regression
- 28 - Independent and dependent variables
- 29 - Linear regression for continuous values
- 30 - Fitting a line
- 31 - Linear least squares
Conclusion
- 32 - Next steps
Related courses
- Machine Learning Foundations: Probability
- Machine Learning & AI Foundations: Linear Regression
- Machine Learning and AI Foundations: Decision Trees with SPSS
- Machine Learning and AI Foundations: Clustering and Association
- Machine Learning and AI Foundations: Classification Modeling
- Artificial Intelligence Foundations: Machine Learning (2018)
- Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference
- Data Science Foundations: Data Mining in R
Related learn paths
- Foundational Math for Machine Learning
- Machine Learning Statistical Foundations Professional Certificate by Wolfram Research
- Develop Your SPSS Skills
- Explore a Career in Data Analysis
- Mastering Executive-Level Data Analytics
- Become a Data Scientist
- Build Essential Data Skills
- Moving from Data Analyst to Data Scientist