English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8h 58m | 3.70 GB

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010.

## Table of Contents

1 Probability and Statistics for Machine Learning – Introduction

2 Topics

3 Orientation to the Machine Learning Foundations Series

4 What Probability Theory Is

5 Events and Sample Spaces

6 Multiple Observations

7 Factorials and Combinatorics

8 Exercises

9 The Law of Large Numbers and the Gambler’s Fallacy

10 Probability Distributions in Statistics

11 Bayesian versus Frequentist Statistics

12 Applications of Probability to Machine Learning

13 Topics

14 Discrete and Continuous Variables

15 Probability Mass Functions

16 Probability Density Functions

17 Exercises on Probability Functions

18 Expected Value

19 Exercises on Expected Value

20 Topics

21 The Mean, a Measure of Central Tendency

22 Medians

23 Modes

24 Quantiles – Percentiles, Quartiles, and Deciles

25 Box-and-Whisker Plots

26 Variance, a Measure of Dispersion

27 Standard Deviation

28 Standard Error

29 Covariance, a Measure of Relatedness

30 Correlation

31 Topics

32 Joint Probability Distribution

33 Marginal Probability

34 Conditional Probability

35 Exercises

36 Chain Rule of Probabilities

37 Independent Random Variables

38 Conditional Independence

39 Topics

40 Uniform

41 Gaussian – Normal and Standard Normal

42 The Central Limit Theorem

43 Log-Normal

44 Exponential and Laplace

45 Binomial and Multinomial

46 Poisson

47 Mixture Distributions

48 Preprocessing Data for Model Input

49 Exercises

50 Topics

51 What Information Theory Is

52 Self-Information, Nats, and Bits

53 Shannon and Differential Entropy

54 Kullback-Leibler Divergence and Cross-Entropy

55 Topics

56 Applications of Statistics to Machine Learning

57 Review of Essential Probability Theory

58 z-scores and Outliers

59 Exercises on z-scores

60 p-values

61 Exercises on p-values

62 Topics

63 Single-Sample t-tests and Degrees of Freedom

64 Independent t-tests

65 Paired t-tests

66 Applications to Machine Learning

67 Exercises

68 Confidence Intervals

69 ANOVA – Analysis of Variance

70 Topics

71 The Pearson Correlation Coefficient

72 R-squared Coefficient of Determination

73 Correlation versus Causation

74 Correcting for Multiple Comparisons

75 Topics

76 Independent versus Dependent Variables

77 Linear Regression to Predict Continuous Values

78 Fitting a Line to Points on a Cartesian Plane

79 Linear Least Squares Exercise

80 Ordinary Least Squares

81 Categorical ‘Dummy’ Features

82 Logistic Regression to Predict Categories

83 Open-Ended Exercises

84 Topics

85 Machine Learning versus Frequentist Statistics

86 When to Use Bayesian Statistics

87 Prior Probabilities

88 Bayes’ Theorem

89 Resources for Further Study of Probability and Statistics

90 Probability and Statistics for Machine Learning – Summary

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