Introduction to Probability and Statistics for Machine Learning
Probability and statistics form the foundation for understanding data and making informed decisions in machine learning. This course will focus on key concepts and techniques that hold significant importance in the realm of deep learning.
Lessons and practices
Probability of Rolling a 2 on a Die
Probability of Rolling 6 on Two Dice
Calculating Probability of Rolling a 6 or 5 on a Dice
Rolling a 6 on One Die and Not a 6 on the Other Die
Calculate Probability of Even or Divisible by Six Rolls
Conditional Probability of Drawing a Heart Given an Ace
Calculate Average Monthly Temperature and Standard Deviation
Calculating Mean and Standard Deviation for Monthly Sales Data
Calculate the Median of Salaries
Increase the Standard Deviation of a Dataset
Analyzing Drug Effectiveness with Descriptive Statistics
Comparing Mean Temperatures of Two Cities
Adjust and Plot PDFs with Different Standard Deviations
Adjusting Mean Values and Plotting PDF
Generate and Visualize Uniform Distribution Sample
Plotting PDF and CDF of Exponential Distribution
Plotting and Analyzing the CDF of a Normal Distribution
Plotting the CDF of Uniform Distribution for Sunlight Hours
Hypothesis Testing for Exam Scores
Perform a One-Sample T-Test on Daily Water Intake
Two Sample T-Test for Teaching Methods
Best-Fit Line for House Prices
Predict Saucer Sightings for Future Months
Best-Fit Line for Temperature and Ice Cream Sales
Predict House Prices Using Linear Regression
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.