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01-Probability and Statistics for Machine Learning - Introduction.mp4
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MP4
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239.7 MB
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02-Topics.mp4
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40.8 MB
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03-1.1 Orientation to the Machine Learning Foundations Series.mp4
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97.8 MB
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04-1.2 What Probability Theory Is.mp4
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204 MB
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05-1.3 Events and Sample Spaces.mp4
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229.1 MB
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06-1.4 Multiple Observations.mp4
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269.2 MB
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07-1.5 Factorials and Combinatorics.mp4
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245.6 MB
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08-1.6 Exercises.mp4
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350.6 MB
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09-1.7 The Law of Large Numbers and the Gambler's Fallacy.mp4
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517.8 MB
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10-1.8 Probability Distributions in Statistics.mp4
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210.7 MB
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11-1.9 Bayesian versus Frequentist Statistics.mp4
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355.6 MB
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12-1.10 Applications of Probability to Machine Learning.mp4
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258.3 MB
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13-Topics.mp4
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38.8 MB
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14-2.1 Discrete and Continuous Variables.mp4
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112.3 MB
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15-2.2 Probability Mass Functions.mp4
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132.3 MB
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16-2.3 Probability Density Functions.mp4
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104.9 MB
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17-2.4 Exercises on Probability Functions.mp4
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147.6 MB
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18-2.5 Expected Value.mp4
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150.9 MB
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19-2.6 Exercises on Expected Value.mp4
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264.1 MB
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20-Topics.mp4
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23.3 MB
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21-3.1 The Mean, a Measure of Central Tendency.mp4
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351.5 MB
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22-3.2 Medians.mp4
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111.3 MB
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23-3.3 Modes.mp4
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266.2 MB
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24-3.4 Quantiles - Percentiles, Quartiles, and Deciles.mp4
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MP4
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334 MB
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25-3.5 Box-and-Whisker Plots.mp4
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MP4
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367.4 MB
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26-3.6 Variance, a Measure of Dispersion.mp4
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467.8 MB
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27-3.7 Standard Deviation.mp4
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155.5 MB
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28-3.8 Standard Error.mp4
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234.9 MB
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29-3.9 Covariance, a Measure of Relatedness.mp4
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525.9 MB
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30-3.10. Correlation.mp4
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375.6 MB
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31-Topics.mp4
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21.9 MB
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32-4.1 Joint Probability Distribution.mp4
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89.2 MB
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33-4.2 Marginal Probability.mp4
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146.8 MB
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34-4.3 Conditional Probability.mp4
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163 MB
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35-4.4 Exercises.mp4
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347.9 MB
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36-4.5 Chain Rule of Probabilities.mp4
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112.3 MB
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37-4.6 Independent Random Variables.mp4
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100.7 MB
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38-4.7 Conditional Independence.mp4
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138.7 MB
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39-Topics.mp4
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46.7 MB
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40-5.1 Uniform.mp4
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130.1 MB
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41-5.2 Gaussian - Normal and Standard Normal.mp4
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376 MB
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42-5.3 The Central Limit Theorem.mp4
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592.9 MB
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43-5.4 Log-Normal.mp4
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113.3 MB
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44-5.5 Exponential and Laplace.mp4
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191.2 MB
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45-5.6 Binomial and Multinomial.mp4
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341.3 MB
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46-5.7 Poisson.mp4
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158.5 MB
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47-5.8 Mixture Distributions.mp4
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150.8 MB
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48-5.9 Preprocessing Data for Model Input.mp4
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124.9 MB
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49-5.10 Exercises.mp4
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48.5 MB
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50-Topics.mp4
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30.4 MB
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51-6.1 What Information Theory Is.mp4
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39.5 MB
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52-6.2 Self-Information, Nats, and Bits.mp4
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171.8 MB
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53-6.3 Shannon and Differential Entropy.mp4
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314.1 MB
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54-6.4 Kullback-Leibler Divergence and Cross-Entropy.mp4
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MP4
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246.7 MB
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55-Topics.mp4
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38.7 MB
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56-7.1 Applications of Statistics to Machine Learning.mp4
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MP4
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357.9 MB
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57-7.2 Review of Essential Probability Theory.mp4
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450.6 MB
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58-7.3 z-scores and Outliers.mp4
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410.7 MB
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59-7.4 Exercises on z-scores.mp4
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231.6 MB
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60-7.5 p-values.mp4
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562.7 MB
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61-7.6 Exercises on p-values.mp4
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231.5 MB
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62-Topics.mp4
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38.9 MB
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63-8.1 Single-Sample t-tests and Degrees of Freedom.mp4
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477.6 MB
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64-8.2 Independent t-tests.mp4
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475.3 MB
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65-8.3 Paired t-tests.mp4
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584 MB
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66-8.4 Applications to Machine Learning.mp4
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302.5 MB
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67-8.5 Exercises.mp4
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245 MB
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68-8.6 Confidence Intervals.mp4
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382.2 MB
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69-8.7 ANOVA - Analysis of Variance.mp4
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165 MB
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70-Topics.mp4
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33.4 MB
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71-9.1 The Pearson Correlation Coefficient.mp4
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260.9 MB
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72-9.2 R-squared Coefficient of Determination.mp4
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144.6 MB
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73-9.3 Correlation versus Causation.mp4
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MP4
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270.5 MB
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74-9.4 Correcting for Multiple Comparisons.mp4
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209.7 MB
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75-Topics.mp4
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50.4 MB
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76-10.1 Independent versus Dependent Variables.mp4
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105.6 MB
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77-10.2 Linear Regression to Predict Continuous Values.mp4
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220.9 MB
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78-10.3 Fitting a Line to Points on a Cartesian Plane.mp4
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538.3 MB
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79-10.4 Linear Least Squares Exercise.mp4
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177.2 MB
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80-10.5 Ordinary Least Squares.mp4
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449.3 MB
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81-10.6 Categorical 'Dummy' Features.mp4
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633.8 MB
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82-10.7 Logistic Regression to Predict Categories.mp4
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458.8 MB
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83-10.8 Open-Ended Exercises.mp4
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163.9 MB
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84-Topics.mp4
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39.6 MB
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85-11.1 Machine Learning versus Frequentist Statistics.mp4
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227.1 MB
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86-11.2 When to Use Bayesian Statistics.mp4
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70.6 MB
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87-11.3 Prior Probabilities.mp4
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166.7 MB
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88-11.4 Bayes' Theorem.mp4
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569 MB
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89-11.5 Resources for Further Study of Probability and Statistics.mp4
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75.2 MB
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90-Probability and Statistics for Machine Learning - Summary.mp4
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73.7 MB
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TutsNode.com.txt
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