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Probability and Statistics for Machine Learning
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[TutsNode.com] - Probability and Statistics for Machine Learning (Size: 20.86 GB) (Files: 180)
[TutsNode.com] - Probability and Statistics for Machine Learning
81-10.6 Categorical 'Dummy' Features.mp4
42-5.3 The Central Limit Theorem.mp4
65-8.3 Paired t-tests.mp4
88-11.4 Bayes' Theorem.mp4
60-7.5 p-values.mp4
78-10.3 Fitting a Line to Points on a Cartesian Plane.mp4
29-3.9 Covariance, a Measure of Relatedness.mp4
09-1.7 The Law of Large Numbers and the Gambler's Fallacy.mp4
63-8.1 Single-Sample t-tests and Degrees of Freedom.mp4
64-8.2 Independent t-tests.mp4
26-3.6 Variance, a Measure of Dispersion.mp4
82-10.7 Logistic Regression to Predict Categories.mp4
57-7.2 Review of Essential Probability Theory.mp4
80-10.5 Ordinary Least Squares.mp4
58-7.3 z-scores and Outliers.mp4
68-8.6 Confidence Intervals.mp4
41-5.2 Gaussian - Normal and Standard Normal.mp4
30-3.10. Correlation.mp4
25-3.5 Box-and-Whisker Plots.mp4
56-7.1 Applications of Statistics to Machine Learning.mp4
11-1.9 Bayesian versus Frequentist Statistics.mp4
21-3.1 The Mean, a Measure of Central Tendency.mp4
08-1.6 Exercises.mp4
35-4.4 Exercises.mp4
45-5.6 Binomial and Multinomial.mp4
24-3.4 Quantiles - Percentiles, Quartiles, and Deciles.mp4
53-6.3 Shannon and Differential Entropy.mp4
66-8.4 Applications to Machine Learning.mp4
73-9.3 Correlation versus Causation.mp4
06-1.4 Multiple Observations.mp4
23-3.3 Modes.mp4
19-2.6 Exercises on Expected Value.mp4
71-9.1 The Pearson Correlation Coefficient.mp4
12-1.10 Applications of Probability to Machine Learning.mp4
54-6.4 Kullback-Leibler Divergence and Cross-Entropy.mp4
07-1.5 Factorials and Combinatorics.mp4
67-8.5 Exercises.mp4
01-Probability and Statistics for Machine Learning - Introduction.mp4
28-3.8 Standard Error.mp4
59-7.4 Exercises on z-scores.mp4
61-7.6 Exercises on p-values.mp4
05-1.3 Events and Sample Spaces.mp4
85-11.1 Machine Learning versus Frequentist Statistics.mp4
77-10.2 Linear Regression to Predict Continuous Values.mp4
10-1.8 Probability Distributions in Statistics.mp4
74-9.4 Correcting for Multiple Comparisons.mp4
04-1.2 What Probability Theory Is.mp4
44-5.5 Exponential and Laplace.mp4
79-10.4 Linear Least Squares Exercise.mp4
52-6.2 Self-Information, Nats, and Bits.mp4
87-11.3 Prior Probabilities.mp4
69-8.7 ANOVA - Analysis of Variance.mp4
83-10.8 Open-Ended Exercises.mp4
34-4.3 Conditional Probability.mp4
46-5.7 Poisson.mp4
27-3.7 Standard Deviation.mp4
18-2.5 Expected Value.mp4
47-5.8 Mixture Distributions.mp4
17-2.4 Exercises on Probability Functions.mp4
33-4.2 Marginal Probability.mp4
72-9.2 R-squared Coefficient of Determination.mp4
38-4.7 Conditional Independence.mp4
15-2.2 Probability Mass Functions.mp4
40-5.1 Uniform.mp4
48-5.9 Preprocessing Data for Model Input.mp4
43-5.4 Log-Normal.mp4
14-2.1 Discrete and Continuous Variables.mp4
36-4.5 Chain Rule of Probabilities.mp4
22-3.2 Medians.mp4
76-10.1 Independent versus Dependent Variables.mp4
16-2.3 Probability Density Functions.mp4
37-4.6 Independent Random Variables.mp4
03-1.1 Orientation to the Machine Learning Foundations Series.mp4
32-4.1 Joint Probability Distribution.mp4
89-11.5 Resources for Further Study of Probability and Statistics.mp4
90-Probability and Statistics for Machine Learning - Summary.mp4
86-11.2 When to Use Bayesian Statistics.mp4
75-Topics.mp4
49-5.10 Exercises.mp4
39-Topics.mp4
02-Topics.mp4
84-Topics.mp4
51-6.1 What Information Theory Is.mp4
62-Topics.mp4
13-Topics.mp4
55-Topics.mp4
70-Topics.mp4
50-Topics.mp4
20-Topics.mp4
31-Topics.mp4
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