Machine Learning Foundations: Statistics

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Terezija Semenski

1:20:25

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01 - Introduction

01 - Foundations of statistics for machine learning.mp4

00:46

02 - What you should know.mp4

01:05

02 - 1. Introduction to Statistics

01 - Defining statistics.mp4

02:37

02 - Applications of statistics in ML.mp4

02:58

03 - Types of data.mp4

03:33

03 - 2. The Summary Statistics

01 - The mean.mp4

03:36

02 - The median.mp4

02:43

03 - The mode.mp4

02:19

04 - The percentile.mp4

02:48

05 - The percentage change.mp4

02:03

06 - The range.mp4

02:33

07 - The variance and the standard deviation.mp4

04:06

08 - The standard error of the mean vs. the standard deviation.mp4

02:38

04 - 3. From Quantiles to Correlation

01 - The quantiles and box plots.mp4

03:01

02 - Missing data.mp4

03:34

03 - The correlation.mp4

02:28

04 - The covariance.mp4

02:43

05 - The correlation coefficient.mp4

02:43

06 - The correlation vs. causation.mp4

02:16

05 - 4. Random Variables and Probability Distribution

01 - Introduction to probability distribution.mp4

02:15

02 - The uniform distribution.mp4

02:19

03 - The normal distribution.mp4

03:00

04 - The Bernoulli distribution.mp4

02:06

05 - The Multinoulli distribution.mp4

02:13

06 - 5. Sampling and Replacement

01 - Selection with replacement.mp4

02:24

02 - Selection without replacement.mp4

02:19

03 - Bootstrapping.mp4

02:42

07 - 6. Linear Regression

01 - Independent and dependent variables.mp4

02:05

02 - Linear regression for continuous values.mp4

02:33

03 - Fitting a line.mp4

02:13

04 - Linear least squares.mp4

03:03

08 - Conclusion

01 - Next steps.mp4

00:43

Description

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.

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Machine Learning

Statistics

Terezija Semenski

Instructor's Courses

I help busy people learn to code without spending hours on searching Google and Stackoverflow. Mathematician and Software Developer with a business mind, a learning mindset, and a passion for people. Currently working as a freelance Educator and Software Developer. I’m also preparing my 8th online course. My previous experience includes working as Software Developer and QA in startup on educational, financial and banking app development projects and in the education sector as IT and Mathematics Teacher.

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.