Foundations of Statistics and Probability for Machine Learning

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Janani Ravi

2:12:43

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01. Course Overview

01. Course Overview.mp4

01:55

02. Understanding Descriptive Statistics and Probability Distributions

02. Prerequisites and Course Outline.mp4

02:30

03. Descriptive Statistics to Understand Data.mp4

03:45

04. Measures of Frequency and Central Tendency.mp4

06:50

05. Measures of Dispersion.mp4

03:56

06. Demo-Measures of Central Tendency.mp4

05:46

07. Demo-Measures of Dispersion.mp4

04:05

08. Probability and the Gaussian Normal Distribution.mp4

04:23

09. Demo-Probability.mp4

03:04

10. Demo-Normal Distribution.mp4

04:33

11. Skewness and Kurtosis.mp4

04:31

12. Demo-Skewness and Kurtosis.mp4

08:04

03. Interpreting Data Using Statistical Test

13. Steps in Hypothesis Testing.mp4

06:29

14. Hypothesis Testing-Lady Tasting Tea.mp4

03:36

15. Type I and Type II Errors.mp4

04:35

16. Introducing t-tests.mp4

04:25

17. Types of t-tests.mp4

05:35

18. Demo-Two Sample t-test Part I.mp4

06:57

19. Demo-Two Sample t-test Part II.mp4

02:50

20. Demo-Paired Samples t-test.mp4

08:03

04. Performing Regression Analysis

21. Connecting the Dots with Linear Regression.mp4

06:16

22. Setting up the Regression Problem.mp4

04:09

23. Interpreting the Results of Regression.mp4

04:10

24. Demo-Exploring the Dataset.mp4

02:47

25. Demo-Regression Analysis Using a Single Predictor.mp4

07:12

26. Demo-Preprocessing Data for Multiple Regression.mp4

06:24

27. Demo-Regression Analysis Using Multiple Predictors.mp4

04:37

28. Summary and Further Study.mp4

01:16

Description

This course will teach you the concepts, theory, and implementation of basic statistics, probability, hypothesis testing, and regression analysis required to build and interpret meaningful machine learning models.

What You'll Learn?

Learning the importance of p-values and test statistics and how these can be used to accept or reject the null hypothesis can lead you to explore the different types of t-tests and learn to choose the right one for your use case.

In this course, Foundations of Statistics and Probability for Machine Learning, you will learn to leverage statistics for exploratory data analysis and hypothesis testing.

First, you will explore measures of central tendency and dispersion including mean, mode, median, range, and standard deviation.

Then, you will explore the basics of probability and probability distributions and learn how skewness and kurtosis can give you important insights into your data.

Next, you will discover how you can perform hypothesis testing and interpret the results of these statistical tests.

Finally, you will learn how to perform and interpret regression models both simple regression with a single predictor and multiple regression with multiple predictors, and you will evaluate your regression models using R-squared and adjusted R-squared and understand the t-statistic and p-value associated with regression coefficients.

When you are finished with this course, you will have the skills and knowledge of statistics and data analysis needed to effectively explore and interpret your data as a precursor to applying machine learning techniques.

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Machine Learning

Statistics

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.