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Crash Course Statistics - Season 1 Episode 36 Supervised Machine Learning

Crash Course Statistics

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Crash Course Statistics

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Crash Course Statistics

Crash Course Statistics - Season 1 Episode 36 Supervised Machine Learning

Crash Course Statistics - Season 1 Episode 36 Supervised Machine Learning

Year: 2019
Genre:
Country: United States of America
Studio: YouTube
Director:
Cast: Adriene Hill
Crew:
Alternative Titles:
Keyword: mathematics, educational, statistics, statistician

Overview: We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes. We'll discuss three popular types of supervised machine learning models: Logistic Regression, Linear discriminant Analysis (or LDA) and K Nearest Neighbors (or KNN). For a broader overview of machine learning, check out our episode in Crash Course Computer Science!

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First Air Date: Jan 24, 2018 Last Air date: Jan 09, 2019 Season: 1 Season Episode: 44 Episode Runtime: 13 minutes Quality: HD IMDb: 2.00 / 10 by 1 users Popularity: 3.252 Language: English

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1. What Is Statistics2018-01-24
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2. Mathematical Thinking2018-01-31
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3. Mean, Median, and Mode: Measures of Central Tendency2018-02-07
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4. Measures of Spread2018-02-14
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5. Charts Are Like Pasta - Data Visualization Part 12018-02-21
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6. Plots, Outliers, and Justin Timberlake: Data Visualization Part 22018-02-28
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7. The Shape of Data: Distributions2018-03-07
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8. Correlation Doesn’t Equal Causation2018-03-14
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9. Controlled Experiments2018-03-21
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10. Sampling Methods and Bias with Surveys2018-03-28
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11. Science Journalism2018-04-11
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12. Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection2018-04-18
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13. Probability Part 1: Rules and Patterns2018-04-25
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14. Probability Part 2: Updating Your Beliefs with Bayes2018-05-02
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15. The Binomial Distribution2018-05-09
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16. Geometric Distributions and The Birthday Paradox2018-05-16
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17. Randomness2018-05-23
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18. Z-Scores and Percentiles2018-05-30
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19. The Normal Distribution2018-06-06
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20. Confidence Intervals2018-06-13
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21. How P-Values Help Us Test Hypotheses2018-06-27
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22. P-Value Problems2018-07-11
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23. Playing with Power: P-Values Pt 32018-07-18
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24. You Know I’m All About that Bayes2018-07-25
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25. Bayes in Science and Everyday Life2018-08-01
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26. Test Statistics2018-08-08
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27. T-Tests: A Matched Pair Made in Heaven2018-08-15
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28. Degrees of Freedom and Effect Sizes2018-08-22
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29. Chi-Square Tests2018-08-29
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30. P-Hacking2018-09-05
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31. The Replication Crisis2018-09-26
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32. Regression2018-10-03
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33. ANOVA2018-10-10
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34. ANOVA Part 2: Dealing with Intersectional Groups2018-10-17
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35. Fitting Models Is like Tetris2018-10-24
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36. Supervised Machine Learning2018-10-31
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37. Unsupervised Machine Learning2018-11-07
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38. Intro to Big Data2018-11-14
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39. Big Data Problems2018-11-21
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40. Statistics in the Courts2018-11-28
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41. Neural Networks2018-12-12
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42. War2018-12-19
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43. When Predictions Fail2019-01-02
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44. When Predictions Succeed2019-01-09
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