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[FreeCourseSite.com] Udemy - Master statistics and machine learning intuition math code

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File Name
Size
0. Websites you may like/[CourseClub.ME].url
122 B
0. Websites you may like/[FCS Forum].url
133 B
0. Websites you may like/[FreeCourseSite.com].url
127 B
01 Introductions/001 [Important] Getting the most out of this course.en.srt
6.3 kB
01 Introductions/001 [Important] Getting the most out of this course.mp4
38 MB
01 Introductions/002 About using MATLAB or Python.en.srt
6.2 kB
01 Introductions/002 About using MATLAB or Python.mp4
39 MB
01 Introductions/003 Statistics guessing game!.en.srt
14 kB
01 Introductions/003 Statistics guessing game!.mp4
80 MB
01 Introductions/003 stats-intro-GuessTheTest.zip
3.7 kB
01 Introductions/004 Using the Q&A forum.en.srt
8.5 kB
01 Introductions/004 Using the Q&A forum.mp4
24 MB
01 Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.en.srt
3.2 kB
01 Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.mp4
8.5 MB
02 Math prerequisites/006 Should you memorize statistical formulas_.en.srt
4.3 kB
02 Math prerequisites/006 Should you memorize statistical formulas_.mp4
28 MB
02 Math prerequisites/007 Arithmetic and exponents.en.srt
5.8 kB
02 Math prerequisites/007 Arithmetic and exponents.mp4
7.6 MB
02 Math prerequisites/008 Scientific notation.en.srt
9.1 kB
02 Math prerequisites/008 Scientific notation.mp4
13 MB
02 Math prerequisites/009 Summation notation.en.srt
6.2 kB
02 Math prerequisites/009 Summation notation.mp4
7.8 MB
02 Math prerequisites/010 Absolute value.en.srt
4.3 kB
02 Math prerequisites/010 Absolute value.mp4
7.0 MB
02 Math prerequisites/011 Natural exponent and logarithm.en.srt
8.4 kB
02 Math prerequisites/011 Natural exponent and logarithm.mp4
12 MB
02 Math prerequisites/012 The logistic function.en.srt
14 kB
02 Math prerequisites/012 The logistic function.mp4
18 MB
02 Math prerequisites/013 Rank and tied-rank.en.srt
10 kB
02 Math prerequisites/013 Rank and tied-rank.mp4
13 MB
03 IMPORTANT_ Download course materials/014 Download materials for the entire course!.en.srt
5.6 kB
03 IMPORTANT_ Download course materials/014 Download materials for the entire course!.mp4
14 MB
03 IMPORTANT_ Download course materials/014 statsML.zip
1.4 MB
04 What are (is_) data_/015 Is _data_ singular or plural_!_!!_!.en.srt
2.4 kB
04 What are (is_) data_/015 Is _data_ singular or plural_!_!!_!.mp4
11 MB
04 What are (is_) data_/016 Where do data come from and what do they mean_.en.srt
8.7 kB
04 What are (is_) data_/016 Where do data come from and what do they mean_.mp4
36 MB
04 What are (is_) data_/017 Types of data_ categorical, numerical, etc.en.srt
22 kB
04 What are (is_) data_/017 Types of data_ categorical, numerical, etc.mp4
60 MB
04 What are (is_) data_/018 Code_ representing types of data on computers.en.srt
14 kB
04 What are (is_) data_/018 Code_ representing types of data on computers.mp4
48 MB
04 What are (is_) data_/019 Sample vs. population data.en.srt
18 kB
04 What are (is_) data_/019 Sample vs. population data.mp4
37 MB
04 What are (is_) data_/020 Samples, case reports, and anecdotes.en.srt
8.0 kB
04 What are (is_) data_/020 Samples, case reports, and anecdotes.mp4
18 MB
04 What are (is_) data_/021 The ethics of making up data.en.srt
11 kB
04 What are (is_) data_/021 The ethics of making up data.mp4
20 MB
05 Visualizing data/022 Bar plots.en.srt
18 kB
05 Visualizing data/022 Bar plots.mp4
37 MB
05 Visualizing data/023 Code_ bar plots.en.srt
26 kB
05 Visualizing data/023 Code_ bar plots.mp4
100 MB
05 Visualizing data/024 Box-and-whisker plots.en.srt
8.1 kB
05 Visualizing data/024 Box-and-whisker plots.mp4
11 MB
05 Visualizing data/025 Code_ box plots.en.srt
13 kB
05 Visualizing data/025 Code_ box plots.mp4
84 MB
05 Visualizing data/026 _Unsupervised learning__ Boxplots of normal and uniform noise.en.srt
3.9 kB
05 Visualizing data/026 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4
8.3 MB
05 Visualizing data/027 Histograms.en.srt
16 kB
05 Visualizing data/027 Histograms.mp4
44 MB
05 Visualizing data/028 Code_ histograms.en.srt
25 kB
05 Visualizing data/028 Code_ histograms.mp4
134 MB
05 Visualizing data/029 _Unsupervised learning__ Histogram proportion.en.srt
3.5 kB
05 Visualizing data/029 _Unsupervised learning__ Histogram proportion.mp4
12 MB
05 Visualizing data/030 Pie charts.en.srt
8.8 kB
05 Visualizing data/030 Pie charts.mp4
17 MB
05 Visualizing data/031 Code_ pie charts.en.srt
20 kB
05 Visualizing data/031 Code_ pie charts.mp4
69 MB
05 Visualizing data/032 When to use lines instead of bars.en.srt
9.0 kB
05 Visualizing data/032 When to use lines instead of bars.mp4
18 MB
05 Visualizing data/033 Linear vs. logarithmic axis scaling.en.srt
13 kB
05 Visualizing data/033 Linear vs. logarithmic axis scaling.mp4
26 MB
05 Visualizing data/034 Code_ line plots.en.srt
11 kB
05 Visualizing data/034 Code_ line plots.mp4
37 MB
05 Visualizing data/035 _Unsupervised learning__ log-scaled plots.en.srt
2.6 kB
05 Visualizing data/035 _Unsupervised learning__ log-scaled plots.mp4
3.8 MB
06 Descriptive statistics/036 Descriptive vs. inferential statistics.en.srt
6.6 kB
06 Descriptive statistics/036 Descriptive vs. inferential statistics.mp4
22 MB
06 Descriptive statistics/037 Accuracy, precision, resolution.en.srt
12 kB
06 Descriptive statistics/037 Accuracy, precision, resolution.mp4
26 MB
06 Descriptive statistics/038 Data distributions.en.srt
17 kB
06 Descriptive statistics/038 Data distributions.mp4
32 MB
06 Descriptive statistics/039 Code_ data from different distributions.en.srt
48 kB
06 Descriptive statistics/039 Code_ data from different distributions.mp4
304 MB
06 Descriptive statistics/040 _Unsupervised learning__ histograms of distributions.en.srt
3.2 kB
06 Descriptive statistics/040 _Unsupervised learning__ histograms of distributions.mp4
10 MB
06 Descriptive statistics/041 The beauty and simplicity of Normal.en.srt
7.9 kB
06 Descriptive statistics/041 The beauty and simplicity of Normal.mp4
10 MB
06 Descriptive statistics/042 Measures of central tendency (mean).en.srt
20 kB
06 Descriptive statistics/042 Measures of central tendency (mean).mp4
39 MB
06 Descriptive statistics/043 Measures of central tendency (median, mode).en.srt
19 kB
06 Descriptive statistics/043 Measures of central tendency (median, mode).mp4
34 MB
06 Descriptive statistics/044 Code_ computing central tendency.en.srt
21 kB
06 Descriptive statistics/044 Code_ computing central tendency.mp4
76 MB
06 Descriptive statistics/045 _Unsupervised learning__ central tendencies with outliers.en.srt
4.5 kB
06 Descriptive statistics/045 _Unsupervised learning__ central tendencies with outliers.mp4
17 MB
06 Descriptive statistics/046 Measures of dispersion (variance, standard deviation).en.srt
27 kB
06 Descriptive statistics/046 Measures of dispersion (variance, standard deviation).mp4
54 MB
06 Descriptive statistics/047 Code_ Computing dispersion.en.srt
39 kB
06 Descriptive statistics/047 Code_ Computing dispersion.mp4
266 MB
06 Descriptive statistics/048 Interquartile range (IQR).en.srt
7.3 kB
06 Descriptive statistics/048 Interquartile range (IQR).mp4
9.9 MB
06 Descriptive statistics/049 Code_ IQR.en.srt
24 kB
06 Descriptive statistics/049 Code_ IQR.mp4
84 MB
06 Descriptive statistics/050 QQ plots.en.srt
11 kB
06 Descriptive statistics/050 QQ plots.mp4
16 MB
06 Descriptive statistics/051 Code_ QQ plots.en.srt
24 kB
06 Descriptive statistics/051 Code_ QQ plots.mp4
90 MB
06 Descriptive statistics/052 Statistical _moments_.en.srt
14 kB
06 Descriptive statistics/052 Statistical _moments_.mp4
22 MB
06 Descriptive statistics/053 Histograms part 2_ Number of bins.en.srt
15 kB
06 Descriptive statistics/053 Histograms part 2_ Number of bins.mp4
24 MB
06 Descriptive statistics/054 Code_ Histogram bins.en.srt
19 kB
06 Descriptive statistics/054 Code_ Histogram bins.mp4
118 MB
06 Descriptive statistics/055 Violin plots.en.srt
5.2 kB
06 Descriptive statistics/055 Violin plots.mp4
6.5 MB
06 Descriptive statistics/056 Code_ violin plots.en.srt
16 kB
06 Descriptive statistics/056 Code_ violin plots.mp4
105 MB
06 Descriptive statistics/057 _Unsupervised learning__ asymmetric violin plots.en.srt
4.0 kB
06 Descriptive statistics/057 _Unsupervised learning__ asymmetric violin plots.mp4
17 MB
06 Descriptive statistics/058 Shannon entropy.en.srt
16 kB
06 Descriptive statistics/058 Shannon entropy.mp4
33 MB
06 Descriptive statistics/059 Code_ entropy.en.srt
32 kB
06 Descriptive statistics/059 Code_ entropy.mp4
110 MB
06 Descriptive statistics/060 _Unsupervised learning__ entropy and number of bins.en.srt
2.1 kB
06 Descriptive statistics/060 _Unsupervised learning__ entropy and number of bins.mp4
8.3 MB
07 Data normalizations and outliers/061 Garbage in, garbage out (GIGO).en.srt
5.9 kB
07 Data normalizations and outliers/061 Garbage in, garbage out (GIGO).mp4
12 MB
07 Data normalizations and outliers/062 Z-score standardization.en.srt
15 kB
07 Data normalizations and outliers/062 Z-score standardization.mp4
36 MB
07 Data normalizations and outliers/063 Code_ z-score.en.srt
20 kB
07 Data normalizations and outliers/063 Code_ z-score.mp4
67 MB
07 Data normalizations and outliers/064 Min-max scaling.en.srt
7.5 kB
07 Data normalizations and outliers/064 Min-max scaling.mp4
12 MB
07 Data normalizations and outliers/065 Code_ min-max scaling.en.srt
13 kB
07 Data normalizations and outliers/065 Code_ min-max scaling.mp4
40 MB
07 Data normalizations and outliers/066 _Unsupervised learning__ Invert the min-max scaling.en.srt
3.8 kB
07 Data normalizations and outliers/066 _Unsupervised learning__ Invert the min-max scaling.mp4
6.8 MB
07 Data normalizations and outliers/067 What are outliers and why are they dangerous_.en.srt
22 kB
07 Data normalizations and outliers/067 What are outliers and why are they dangerous_.mp4
43 MB
07 Data normalizations and outliers/068 Removing outliers_ z-score method.en.srt
15 kB
07 Data normalizations and outliers/068 Removing outliers_ z-score method.mp4
34 MB
07 Data normalizations and outliers/069 The modified z-score method.en.srt
6.1 kB
07 Data normalizations and outliers/069 The modified z-score method.mp4
9.7 MB
07 Data normalizations and outliers/070 Code_ z-score for outlier removal.en.srt
35 kB
07 Data normalizations and outliers/070 Code_ z-score for outlier removal.mp4
137 MB
07 Data normalizations and outliers/071 _Unsupervised learning__ z vs. modified-z.en.srt
4.0 kB
07 Data normalizations and outliers/071 _Unsupervised learning__ z vs. modified-z.mp4
9.1 MB
07 Data normalizations and outliers/072 Multivariate outlier detection.en.srt
15 kB
07 Data normalizations and outliers/072 Multivariate outlier detection.mp4
25 MB
07 Data normalizations and outliers/073 Code_ Euclidean distance for outlier removal.en.srt
13 kB
07 Data normalizations and outliers/073 Code_ Euclidean distance for outlier removal.mp4
44 MB
07 Data normalizations and outliers/074 Removing outliers by data trimming.en.srt
8.9 kB
07 Data normalizations and outliers/074 Removing outliers by data trimming.mp4
17 MB
07 Data normalizations and outliers/075 Code_ Data trimming to remove outliers.en.srt
17 kB
07 Data normalizations and outliers/075 Code_ Data trimming to remove outliers.mp4
65 MB
07 Data normalizations and outliers/076 Non-parametric solutions to outliers.en.srt
6.6 kB
07 Data normalizations and outliers/076 Non-parametric solutions to outliers.mp4
23 MB
07 Data normalizations and outliers/077 An outlier lecture on personal accountability.en.srt
4.3 kB
07 Data normalizations and outliers/077 An outlier lecture on personal accountability.mp4
18 MB
08 Probability theory/078 What is probability_.en.srt
19 kB
08 Probability theory/078 What is probability_.mp4
41 MB
08 Probability theory/079 Probability vs. proportion.en.srt
15 kB
08 Probability theory/079 Probability vs. proportion.mp4
38 MB
08 Probability theory/080 Computing probabilities.en.srt
16 kB
08 Probability theory/080 Computing probabilities.mp4
38 MB
08 Probability theory/081 Code_ compute probabilities.en.srt
23 kB
08 Probability theory/081 Code_ compute probabilities.mp4
137 MB
08 Probability theory/082 Probability and odds.en.srt
7.2 kB
08 Probability theory/082 Probability and odds.mp4
12 MB
08 Probability theory/083 _Unsupervised learning__ probabilities of odds-space.en.srt
3.3 kB
08 Probability theory/083 _Unsupervised learning__ probabilities of odds-space.mp4
6.0 MB
08 Probability theory/084 Probability mass vs. density.en.srt
19 kB
08 Probability theory/084 Probability mass vs. density.mp4
134 MB
08 Probability theory/085 Code_ compute probability mass functions.en.srt
17 kB
08 Probability theory/085 Code_ compute probability mass functions.mp4
66 MB
08 Probability theory/086 Cumulative probability distributions.en.srt
16 kB
08 Probability theory/086 Cumulative probability distributions.mp4
37 MB
08 Probability theory/087 Code_ cdfs and pdfs.en.srt
15 kB
08 Probability theory/087 Code_ cdfs and pdfs.mp4
42 MB
08 Probability theory/088 _Unsupervised learning__ cdf's for various distributions.en.srt
3.4 kB
08 Probability theory/088 _Unsupervised learning__ cdf's for various distributions.mp4
9.3 MB
08 Probability theory/089 Creating sample estimate distributions.en.srt
29 kB
08 Probability theory/089 Creating sample estimate distributions.mp4
125 MB
08 Probability theory/090 Monte Carlo sampling.en.srt
4.0 kB
08 Probability theory/090 Monte Carlo sampling.mp4
16 MB
08 Probability theory/091 Sampling variability, noise, and other annoyances.en.srt
14 kB
08 Probability theory/091 Sampling variability, noise, and other annoyances.mp4
106 MB
08 Probability theory/092 Code_ sampling variability.en.srt
40 kB
08 Probability theory/092 Code_ sampling variability.mp4
155 MB
08 Probability theory/093 Expected value.en.srt
16 kB
08 Probability theory/093 Expected value.mp4
60 MB
08 Probability theory/094 Conditional probability.en.srt
20 kB
08 Probability theory/094 Conditional probability.mp4
86 MB
08 Probability theory/095 Code_ conditional probabilities.en.srt
31 kB
08 Probability theory/095 Code_ conditional probabilities.mp4
115 MB
08 Probability theory/096 Tree diagrams for conditional probabilities.en.srt
10 kB
08 Probability theory/096 Tree diagrams for conditional probabilities.mp4
14 MB
08 Probability theory/097 The Law of Large Numbers.en.srt
15 kB
08 Probability theory/097 The Law of Large Numbers.mp4
41 MB
08 Probability theory/098 Code_ Law of Large Numbers in action.en.srt
29 kB
08 Probability theory/098 Code_ Law of Large Numbers in action.mp4
166 MB
08 Probability theory/099 The Central Limit Theorem.en.srt
16 kB
08 Probability theory/099 The Central Limit Theorem.mp4
27 MB
08 Probability theory/100 Code_ the CLT in action.en.srt
24 kB
08 Probability theory/100 Code_ the CLT in action.mp4
94 MB
08 Probability theory/101 _Unsupervised learning__ Averaging pairs of numbers.en.srt
3.3 kB
08 Probability theory/101 _Unsupervised learning__ Averaging pairs of numbers.mp4
9.5 MB
09 Hypothesis testing/102 IVs, DVs, models, and other stats lingo.en.srt
25 kB
09 Hypothesis testing/102 IVs, DVs, models, and other stats lingo.mp4
92 MB
09 Hypothesis testing/103 What is an hypothesis and how do you specify one_.en.srt
24 kB
09 Hypothesis testing/103 What is an hypothesis and how do you specify one_.mp4
49 MB
09 Hypothesis testing/104 Sample distributions under null and alternative hypotheses.en.srt
15 kB
09 Hypothesis testing/104 Sample distributions under null and alternative hypotheses.mp4
44 MB
09 Hypothesis testing/105 P-values_ definition, tails, and misinterpretations.en.srt
28 kB
09 Hypothesis testing/105 P-values_ definition, tails, and misinterpretations.mp4
132 MB
09 Hypothesis testing/106 P-z combinations that you should memorize.en.srt
9.4 kB
09 Hypothesis testing/106 P-z combinations that you should memorize.mp4
17 MB
09 Hypothesis testing/107 Degrees of freedom.en.srt
19 kB
09 Hypothesis testing/107 Degrees of freedom.mp4
33 MB
09 Hypothesis testing/108 Type 1 and Type 2 errors.en.srt
23 kB
09 Hypothesis testing/108 Type 1 and Type 2 errors.mp4
46 MB
09 Hypothesis testing/109 Parametric vs. non-parametric tests.en.srt
13 kB
09 Hypothesis testing/109 Parametric vs. non-parametric tests.mp4
88 MB
09 Hypothesis testing/110 Multiple comparisons and Bonferroni correction.en.srt
13 kB
09 Hypothesis testing/110 Multiple comparisons and Bonferroni correction.mp4
30 MB
09 Hypothesis testing/111 Statistical vs. theoretical vs. clinical significance.en.srt
10 kB
09 Hypothesis testing/111 Statistical vs. theoretical vs. clinical significance.mp4
19 MB
09 Hypothesis testing/112 Cross-validation.en.srt
17 kB
09 Hypothesis testing/112 Cross-validation.mp4
28 MB
09 Hypothesis testing/113 Statistical significance vs. classification accuracy.en.srt
18 kB
09 Hypothesis testing/113 Statistical significance vs. classification accuracy.mp4
43 MB
10 The t-test family/114 Purpose and interpretation of the t-test.en.srt
20 kB
10 The t-test family/114 Purpose and interpretation of the t-test.mp4
32 MB
10 The t-test family/115 One-sample t-test.en.srt
12 kB
10 The t-test family/115 One-sample t-test.mp4
54 MB
10 The t-test family/116 Code_ One-sample t-test.en.srt
33 kB
10 The t-test family/116 Code_ One-sample t-test.mp4
158 MB
10 The t-test family/117 _Unsupervised learning__ The role of variance.en.srt
4.3 kB
10 The t-test family/117 _Unsupervised learning__ The role of variance.mp4
29 MB
10 The t-test family/118 Two-samples t-test.en.srt
20 kB
10 The t-test family/118 Two-samples t-test.mp4
94 MB
10 The t-test family/119 Code_ Two-samples t-test.en.srt
34 kB
10 The t-test family/119 Code_ Two-samples t-test.mp4
212 MB
10 The t-test family/120 _Unsupervised learning__ Importance of N for t-test.en.srt
7.1 kB
10 The t-test family/120 _Unsupervised learning__ Importance of N for t-test.mp4
20 MB
10 The t-test family/121 Wilcoxon signed-rank (nonparametric t-test).en.srt
11 kB
10 The t-test family/121 Wilcoxon signed-rank (nonparametric t-test).mp4
30 MB
10 The t-test family/122 Code_ Signed-rank test.en.srt
28 kB
10 The t-test family/122 Code_ Signed-rank test.mp4
162 MB
10 The t-test family/123 Mann-Whitney U test (nonparametric t-test).en.srt
9.2 kB
10 The t-test family/123 Mann-Whitney U test (nonparametric t-test).mp4
20 MB
10 The t-test family/124 Code_ Mann-Whitney U test.en.srt
8.1 kB
10 The t-test family/124 Code_ Mann-Whitney U test.mp4
52 MB
10 The t-test family/125 Permutation testing for t-test significance.en.srt
17 kB
10 The t-test family/125 Permutation testing for t-test significance.mp4
64 MB
10 The t-test family/126 Code_ permutation testing.en.srt
39 kB
10 The t-test family/126 Code_ permutation testing.mp4
241 MB
10 The t-test family/127 _Unsupervised learning__ How many permutations_.en.srt
8.1 kB
10 The t-test family/127 _Unsupervised learning__ How many permutations_.mp4
55 MB
11 Confidence intervals on parameters/128 What are confidence intervals and why do we need them_.en.srt
14 kB
11 Confidence intervals on parameters/128 What are confidence intervals and why do we need them_.mp4
30 MB
11 Confidence intervals on parameters/129 Computing confidence intervals via formula.en.srt
10 kB
11 Confidence intervals on parameters/129 Computing confidence intervals via formula.mp4
17 MB
11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.en.srt
27 kB
11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.mp4
150 MB
11 Confidence intervals on parameters/131 Confidence intervals via bootstrapping (resampling).en.srt
13 kB
11 Confidence intervals on parameters/131 Confidence intervals via bootstrapping (resampling).mp4
54 MB
11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.en.srt
23 kB
11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.mp4
137 MB
11 Confidence intervals on parameters/133 _Unsupervised learning__ Confidence intervals for variance.en.srt
2.0 kB
11 Confidence intervals on parameters/133 _Unsupervised learning__ Confidence intervals for variance.mp4
8.6 MB
11 Confidence intervals on parameters/134 Misconceptions about confidence intervals.en.srt
9.4 kB
11 Confidence intervals on parameters/134 Misconceptions about confidence intervals.mp4
19 MB
12 Correlation/135 Motivation and description of correlation.en.srt
28 kB
12 Correlation/135 Motivation and description of correlation.mp4
97 MB
12 Correlation/136 Covariance and correlation_ formulas.en.srt
22 kB
12 Correlation/136 Covariance and correlation_ formulas.mp4
42 MB
12 Correlation/137 Code_ correlation coefficient.en.srt
42 kB
12 Correlation/137 Code_ correlation coefficient.mp4
215 MB
12 Correlation/138 Code_ Simulate data with specified correlation.en.srt
21 kB
12 Correlation/138 Code_ Simulate data with specified correlation.mp4
136 MB
12 Correlation/139 Correlation matrix.en.srt
14 kB
12 Correlation/139 Correlation matrix.mp4
31 MB
12 Correlation/140 Code_ correlation matrix.en.srt
33 kB
12 Correlation/140 Code_ correlation matrix.mp4
283 MB
12 Correlation/141 _Unsupervised learning__ average correlation matrices.en.srt
4.2 kB
12 Correlation/141 _Unsupervised learning__ average correlation matrices.mp4
18 MB
12 Correlation/142 _Unsupervised learning__ correlation to covariance matrix.en.srt
6.0 kB
12 Correlation/142 _Unsupervised learning__ correlation to covariance matrix.mp4
10 MB
12 Correlation/143 Partial correlation.en.srt
16 kB
12 Correlation/143 Partial correlation.mp4
60 MB
12 Correlation/144 Code_ partial correlation.en.srt
31 kB
12 Correlation/144 Code_ partial correlation.mp4
108 MB
12 Correlation/145 The problem with Pearson.en.srt
10 kB
12 Correlation/145 The problem with Pearson.mp4
17 MB
12 Correlation/146 Nonparametric correlation_ Spearman rank.en.srt
11 kB
12 Correlation/146 Nonparametric correlation_ Spearman rank.mp4
24 MB
12 Correlation/147 Fisher-Z transformation for correlations.en.srt
10 kB
12 Correlation/147 Fisher-Z transformation for correlations.mp4
29 MB
12 Correlation/148 Code_ Spearman correlation and Fisher-Z.en.srt
12 kB
12 Correlation/148 Code_ Spearman correlation and Fisher-Z.mp4
43 MB
12 Correlation/149 _Unsupervised learning__ Spearman correlation.en.srt
1.9 kB
12 Correlation/149 _Unsupervised learning__ Spearman correlation.mp4
16 MB
12 Correlation/150 _Unsupervised learning__ confidence interval on correlation.en.srt
3.4 kB
12 Correlation/150 _Unsupervised learning__ confidence interval on correlation.mp4
8.9 MB
12 Correlation/151 Kendall's correlation for ordinal data.en.srt
16 kB
12 Correlation/151 Kendall's correlation for ordinal data.mp4
30 MB
12 Correlation/152 Code_ Kendall correlation.en.srt
28 kB
12 Correlation/152 Code_ Kendall correlation.mp4
184 MB
12 Correlation/153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.en.srt
3.5 kB
12 Correlation/153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4
15 MB
12 Correlation/154 Cosine similarity.en.srt
7.8 kB
12 Correlation/154 Cosine similarity.mp4
14 MB
12 Correlation/155 Code_ Cosine similarity vs. Pearson correlation.en.srt
32 kB
12 Correlation/155 Code_ Cosine similarity vs. Pearson correlation.mp4
102 MB
13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.en.srt
27 kB
13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.mp4
138 MB
13 Analysis of Variance (ANOVA)/157 ANOVA intro, part 2.en.srt
30 kB
13 Analysis of Variance (ANOVA)/157 ANOVA intro, part 2.mp4
85 MB
13 Analysis of Variance (ANOVA)/158 Sum of squares.en.srt
27 kB
13 Analysis of Variance (ANOVA)/158 Sum of squares.mp4
46 MB
13 Analysis of Variance (ANOVA)/159 The F-test and the ANOVA table.en.srt
11 kB
13 Analysis of Variance (ANOVA)/159 The F-test and the ANOVA table.mp4
20 MB
13 Analysis of Variance (ANOVA)/160 The omnibus F-test and post-hoc comparisons.en.srt
20 kB
13 Analysis of Variance (ANOVA)/160 The omnibus F-test and post-hoc comparisons.mp4
64 MB
13 Analysis of Variance (ANOVA)/161 The two-way ANOVA.en.srt
31 kB
13 Analysis of Variance (ANOVA)/161 The two-way ANOVA.mp4
105 MB
13 Analysis of Variance (ANOVA)/162 One-way ANOVA example.en.srt
22 kB
13 Analysis of Variance (ANOVA)/162 One-way ANOVA example.mp4
44 MB
13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).en.srt
27 kB
13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).mp4
173 MB
13 Analysis of Variance (ANOVA)/164 Code_ One-way repeated-measures ANOVA.en.srt
19 kB
13 Analysis of Variance (ANOVA)/164 Code_ One-way repeated-measures ANOVA.mp4
73 MB
13 Analysis of Variance (ANOVA)/165 Two-way ANOVA example.en.srt
17 kB
13 Analysis of Variance (ANOVA)/165 Two-way ANOVA example.mp4
36 MB
13 Analysis of Variance (ANOVA)/166 Code_ Two-way mixed ANOVA.en.srt
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13 Analysis of Variance (ANOVA)/166 Code_ Two-way mixed ANOVA.mp4
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14 Regression/167 Introduction to GLM _ regression.en.srt
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14 Regression/167 Introduction to GLM _ regression.mp4
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14 Regression/168 Least-squares solution to the GLM.en.srt
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14 Regression/168 Least-squares solution to the GLM.mp4
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14 Regression/169 Evaluating regression models_ R2 and F.en.srt
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14 Regression/169 Evaluating regression models_ R2 and F.mp4
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14 Regression/170 Simple regression.en.srt
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14 Regression/170 Simple regression.mp4
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14 Regression/171 Code_ simple regression.en.srt
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14 Regression/171 Code_ simple regression.mp4
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14 Regression/172 _Unsupervised learning__ Compute R2 and F.en.srt
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14 Regression/172 _Unsupervised learning__ Compute R2 and F.mp4
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14 Regression/173 Multiple regression.en.srt
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14 Regression/173 Multiple regression.mp4
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14 Regression/174 Standardizing regression coefficients.en.srt
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14 Regression/174 Standardizing regression coefficients.mp4
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14 Regression/175 Code_ Multiple regression.en.srt
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14 Regression/175 Code_ Multiple regression.mp4
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14 Regression/176 Polynomial regression models.en.srt
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14 Regression/176 Polynomial regression models.mp4
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14 Regression/177 Code_ polynomial modeling.en.srt
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14 Regression/177 Code_ polynomial modeling.mp4
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14 Regression/178 _Unsupervised learning__ Polynomial design matrix.en.srt
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14 Regression/178 _Unsupervised learning__ Polynomial design matrix.mp4
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14 Regression/179 Logistic regression.en.srt
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14 Regression/179 Logistic regression.mp4
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14 Regression/180 Code_ Logistic regression.en.srt
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14 Regression/180 Code_ Logistic regression.mp4
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14 Regression/181 Under- and over-fitting.en.srt
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14 Regression/181 Under- and over-fitting.mp4
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14 Regression/182 _Unsupervised learning__ Overfit data.en.srt
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14 Regression/182 _Unsupervised learning__ Overfit data.mp4
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14 Regression/183 Comparing _nested_ models.en.srt
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14 Regression/183 Comparing _nested_ models.mp4
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14 Regression/184 What to do about missing data.en.srt
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14 Regression/184 What to do about missing data.mp4
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15 Statistical power and sample sizes/185 What is statistical power and why is it important_.en.srt
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15 Statistical power and sample sizes/185 What is statistical power and why is it important_.mp4
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15 Statistical power and sample sizes/186 Estimating statistical power and sample size.en.srt
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15 Statistical power and sample sizes/186 Estimating statistical power and sample size.mp4
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15 Statistical power and sample sizes/187 Compute power and sample size using G_Power.en.srt
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15 Statistical power and sample sizes/187 Compute power and sample size using G_Power.mp4
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16 Clustering and dimension-reduction/188 K-means clustering.en.srt
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16 Clustering and dimension-reduction/188 K-means clustering.mp4
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16 Clustering and dimension-reduction/189 Code_ k-means clustering.en.srt
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16 Clustering and dimension-reduction/189 Code_ k-means clustering.mp4
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16 Clustering and dimension-reduction/190 _Unsupervised learning__ K-means and normalization.en.srt
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16 Clustering and dimension-reduction/190 _Unsupervised learning__ K-means and normalization.mp4
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16 Clustering and dimension-reduction/191 _Unsupervised learning__ K-means on a Gauss blur.en.srt
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16 Clustering and dimension-reduction/191 _Unsupervised learning__ K-means on a Gauss blur.mp4
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16 Clustering and dimension-reduction/192 Clustering via dbscan.en.srt
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16 Clustering and dimension-reduction/192 Clustering via dbscan.mp4
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16 Clustering and dimension-reduction/193 Code_ dbscan.en.srt
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16 Clustering and dimension-reduction/193 Code_ dbscan.mp4
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16 Clustering and dimension-reduction/194 _Unsupervised learning__ dbscan vs. k-means.en.srt
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16 Clustering and dimension-reduction/194 _Unsupervised learning__ dbscan vs. k-means.mp4
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16 Clustering and dimension-reduction/195 K-nearest neighbor classification.en.srt
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16 Clustering and dimension-reduction/195 K-nearest neighbor classification.mp4
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16 Clustering and dimension-reduction/196 Code_ KNN.en.srt
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16 Clustering and dimension-reduction/196 Code_ KNN.mp4
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16 Clustering and dimension-reduction/197 Principal components analysis (PCA).en.srt
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16 Clustering and dimension-reduction/197 Principal components analysis (PCA).mp4
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16 Clustering and dimension-reduction/198 Code_ PCA.en.srt
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16 Clustering and dimension-reduction/198 Code_ PCA.mp4
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16 Clustering and dimension-reduction/199 _Unsupervised learning__ K-means on PC data.en.srt
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16 Clustering and dimension-reduction/199 _Unsupervised learning__ K-means on PC data.mp4
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16 Clustering and dimension-reduction/200 Independent components analysis (ICA).en.srt
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16 Clustering and dimension-reduction/200 Independent components analysis (ICA).mp4
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16 Clustering and dimension-reduction/201 Code_ ICA.en.srt
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16 Clustering and dimension-reduction/201 Code_ ICA.mp4
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17 Signal detection theory/202 The two perspectives of the world.en.srt
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17 Signal detection theory/202 The two perspectives of the world.mp4
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17 Signal detection theory/203 d-prime.en.srt
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17 Signal detection theory/203 d-prime.mp4
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17 Signal detection theory/204 Code_ d-prime.en.srt
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17 Signal detection theory/204 Code_ d-prime.mp4
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17 Signal detection theory/205 Response bias.en.srt
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17 Signal detection theory/205 Response bias.mp4
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17 Signal detection theory/206 Code_ Response bias.en.srt
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17 Signal detection theory/206 Code_ Response bias.mp4
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17 Signal detection theory/207 Receiver operating characteristics (ROC).en.srt
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17 Signal detection theory/207 Receiver operating characteristics (ROC).mp4
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17 Signal detection theory/208 Code_ ROC curves.en.srt
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17 Signal detection theory/208 Code_ ROC curves.mp4
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17 Signal detection theory/209 _Unsupervised learning__ Make this plot look nicer!.en.srt
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17 Signal detection theory/209 _Unsupervised learning__ Make this plot look nicer!.mp4
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18 Bonus section/210 About deep learning.html
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18 Bonus section/211 Bonus content.html
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