Name: Crash Course Statistics - Season 1 Episode 37 Unsupervised Machine Learning
Released: 01 Jan 1970
Rating: 2.00
Cast: Adriene Hill

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Crash Course Statistics - Season 1 Episode 37 Unsupervised Machine Learning

Trailer

Today we're going to discuss how machine learning can be used to group and label information even if those labels don't exist. We'll explore two types of clustering used in Unsupervised Machine Learning: k-means and Hierarchical clustering, and show how they can be used in many ways - from book suggestions and medical interventions, to giving people better deals on pizza!

Year: 2019

Genre:

Country: United States of America

Studio: YouTube

Director:

Cast: Adriene Hill

Crew:

First Air Date: Jan 24, 2018

Last Air date: Jan 09, 2019

Season: 1 Season

Episode: 44 Episode

Runtime: 13 minutes

IMDb: 2.00/10 by 1.00 users

Popularity: 0.5311

Language: English

Keyword : mathematics, educational, statistics, statistician

Season Season 1

Episode What Is Statistics Mathematical Thinking Mean, Median, and Mode: Measures of Central Tendency Measures of Spread Charts Are Like Pasta - Data Visualization Part 1 Plots, Outliers, and Justin Timberlake: Data Visualization Part 2 The Shape of Data: Distributions Correlation Doesn’t Equal Causation Controlled Experiments Sampling Methods and Bias with Surveys Science Journalism Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection Probability Part 1: Rules and Patterns Probability Part 2: Updating Your Beliefs with Bayes The Binomial Distribution Geometric Distributions and The Birthday Paradox Randomness Z-Scores and Percentiles The Normal Distribution Confidence Intervals How P-Values Help Us Test Hypotheses P-Value Problems Playing with Power: P-Values Pt 3 You Know I’m All About that Bayes Bayes in Science and Everyday Life Test Statistics T-Tests: A Matched Pair Made in Heaven Degrees of Freedom and Effect Sizes Chi-Square Tests P-Hacking The Replication Crisis Regression ANOVA ANOVA Part 2: Dealing with Intersectional Groups Fitting Models Is like Tetris Supervised Machine Learning Unsupervised Machine Learning Intro to Big Data Big Data Problems Statistics in the Courts Neural Networks War When Predictions Fail When Predictions Succeed