Foundations of Statistics and Probability for Machine Learning
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.
Table of contents
- Version Check 0m
- Prerequisites and Course Outline 3m
- Descriptive Statistics to Understand Data 4m
- Measures of Frequency and Central Tendency 7m
- Measures of Dispersion 4m
- Demo: Measures of Central Tendency 6m
- Demo: Measures of Dispersion 4m
- Probability and the Gaussian Normal Distribution 4m
- Demo: Probability 3m
- Demo: Normal Distribution 5m
- Skewness and Kurtosis 5m
- Demo: Skewness and Kurtosis 8m
- Connecting the Dots with Linear Regression 6m
- Setting up the Regression Problem 4m
- Interpreting the Results of Regression 4m
- Demo: Exploring the Dataset 3m
- Demo: Regression Analysis Using a Single Predictor 7m
- Demo: Preprocessing Data for Multiple Regression 6m
- Demo: Regression Analysis Using Multiple Predictors 5m
- Summary and Further Study 1m