|
Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
|
| [CourseClub.ME].url |
122B |
| [FCS Forum].url |
133B |
| [FreeCourseSite.com].url |
127B |
| 001 [Important] Getting the most out of this course.en.srt |
6.30KB |
| 001 [Important] Getting the most out of this course.mp4 |
38.04MB |
| 002 About using MATLAB or Python.en.srt |
6.17KB |
| 002 About using MATLAB or Python.mp4 |
38.91MB |
| 003 Statistics guessing game!.en.srt |
13.88KB |
| 003 Statistics guessing game!.mp4 |
80.31MB |
| 003 stats-intro-GuessTheTest.zip |
3.72KB |
| 004 Using the Q&A forum.en.srt |
8.48KB |
| 004 Using the Q&A forum.mp4 |
24.47MB |
| 005 (optional) Entering time-stamped notes in the Udemy video player.en.srt |
3.23KB |
| 005 (optional) Entering time-stamped notes in the Udemy video player.mp4 |
8.46MB |
| 006 Should you memorize statistical formulas_.en.srt |
4.32KB |
| 006 Should you memorize statistical formulas_.mp4 |
28.04MB |
| 007 Arithmetic and exponents.en.srt |
5.85KB |
| 007 Arithmetic and exponents.mp4 |
7.62MB |
| 008 Scientific notation.en.srt |
9.10KB |
| 008 Scientific notation.mp4 |
12.96MB |
| 009 Summation notation.en.srt |
6.25KB |
| 009 Summation notation.mp4 |
7.80MB |
| 010 Absolute value.en.srt |
4.34KB |
| 010 Absolute value.mp4 |
6.97MB |
| 011 Natural exponent and logarithm.en.srt |
8.38KB |
| 011 Natural exponent and logarithm.mp4 |
12.28MB |
| 012 The logistic function.en.srt |
13.67KB |
| 012 The logistic function.mp4 |
18.03MB |
| 013 Rank and tied-rank.en.srt |
9.96KB |
| 013 Rank and tied-rank.mp4 |
12.94MB |
| 014 Download materials for the entire course!.en.srt |
5.62KB |
| 014 Download materials for the entire course!.mp4 |
14.52MB |
| 014 statsML.zip |
1.42MB |
| 015 Is _data_ singular or plural_!_!!_!.en.srt |
2.42KB |
| 015 Is _data_ singular or plural_!_!!_!.mp4 |
10.89MB |
| 016 Where do data come from and what do they mean_.en.srt |
8.74KB |
| 016 Where do data come from and what do they mean_.mp4 |
35.62MB |
| 017 Types of data_ categorical, numerical, etc.en.srt |
21.77KB |
| 017 Types of data_ categorical, numerical, etc.mp4 |
59.62MB |
| 018 Code_ representing types of data on computers.en.srt |
13.67KB |
| 018 Code_ representing types of data on computers.mp4 |
47.94MB |
| 019 Sample vs. population data.en.srt |
17.89KB |
| 019 Sample vs. population data.mp4 |
37.27MB |
| 020 Samples, case reports, and anecdotes.en.srt |
7.98KB |
| 020 Samples, case reports, and anecdotes.mp4 |
17.88MB |
| 021 The ethics of making up data.en.srt |
10.72KB |
| 021 The ethics of making up data.mp4 |
19.76MB |
| 022 Bar plots.en.srt |
17.75KB |
| 022 Bar plots.mp4 |
37.01MB |
| 023 Code_ bar plots.en.srt |
26.48KB |
| 023 Code_ bar plots.mp4 |
100.24MB |
| 024 Box-and-whisker plots.en.srt |
8.15KB |
| 024 Box-and-whisker plots.mp4 |
11.21MB |
| 025 Code_ box plots.en.srt |
13.30KB |
| 025 Code_ box plots.mp4 |
83.68MB |
| 026 _Unsupervised learning__ Boxplots of normal and uniform noise.en.srt |
3.89KB |
| 026 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4 |
8.27MB |
| 027 Histograms.en.srt |
16.45KB |
| 027 Histograms.mp4 |
43.91MB |
| 028 Code_ histograms.en.srt |
25.25KB |
| 028 Code_ histograms.mp4 |
133.75MB |
| 029 _Unsupervised learning__ Histogram proportion.en.srt |
3.54KB |
| 029 _Unsupervised learning__ Histogram proportion.mp4 |
11.83MB |
| 030 Pie charts.en.srt |
8.83KB |
| 030 Pie charts.mp4 |
16.63MB |
| 031 Code_ pie charts.en.srt |
20.19KB |
| 031 Code_ pie charts.mp4 |
69.24MB |
| 032 When to use lines instead of bars.en.srt |
8.98KB |
| 032 When to use lines instead of bars.mp4 |
18.08MB |
| 033 Linear vs. logarithmic axis scaling.en.srt |
12.99KB |
| 033 Linear vs. logarithmic axis scaling.mp4 |
25.66MB |
| 034 Code_ line plots.en.srt |
11.32KB |
| 034 Code_ line plots.mp4 |
37.42MB |
| 035 _Unsupervised learning__ log-scaled plots.en.srt |
2.57KB |
| 035 _Unsupervised learning__ log-scaled plots.mp4 |
3.75MB |
| 036 Descriptive vs. inferential statistics.en.srt |
6.63KB |
| 036 Descriptive vs. inferential statistics.mp4 |
21.56MB |
| 037 Accuracy, precision, resolution.en.srt |
11.88KB |
| 037 Accuracy, precision, resolution.mp4 |
25.54MB |
| 038 Data distributions.en.srt |
17.43KB |
| 038 Data distributions.mp4 |
32.14MB |
| 039 Code_ data from different distributions.en.srt |
47.83KB |
| 039 Code_ data from different distributions.mp4 |
303.53MB |
| 040 _Unsupervised learning__ histograms of distributions.en.srt |
3.19KB |
| 040 _Unsupervised learning__ histograms of distributions.mp4 |
10.21MB |
| 041 The beauty and simplicity of Normal.en.srt |
7.93KB |
| 041 The beauty and simplicity of Normal.mp4 |
10.31MB |
| 042 Measures of central tendency (mean).en.srt |
19.78KB |
| 042 Measures of central tendency (mean).mp4 |
38.91MB |
| 043 Measures of central tendency (median, mode).en.srt |
18.95KB |
| 043 Measures of central tendency (median, mode).mp4 |
34.45MB |
| 044 Code_ computing central tendency.en.srt |
20.95KB |
| 044 Code_ computing central tendency.mp4 |
76.27MB |
| 045 _Unsupervised learning__ central tendencies with outliers.en.srt |
4.48KB |
| 045 _Unsupervised learning__ central tendencies with outliers.mp4 |
16.79MB |
| 046 Measures of dispersion (variance, standard deviation).en.srt |
27.35KB |
| 046 Measures of dispersion (variance, standard deviation).mp4 |
54.41MB |
| 047 Code_ Computing dispersion.en.srt |
38.67KB |
| 047 Code_ Computing dispersion.mp4 |
266.53MB |
| 048 Interquartile range (IQR).en.srt |
7.29KB |
| 048 Interquartile range (IQR).mp4 |
9.91MB |
| 049 Code_ IQR.en.srt |
24.41KB |
| 049 Code_ IQR.mp4 |
83.65MB |
| 050 QQ plots.en.srt |
10.58KB |
| 050 QQ plots.mp4 |
16.34MB |
| 051 Code_ QQ plots.en.srt |
24.47KB |
| 051 Code_ QQ plots.mp4 |
90.55MB |
| 052 Statistical _moments_.en.srt |
13.63KB |
| 052 Statistical _moments_.mp4 |
21.81MB |
| 053 Histograms part 2_ Number of bins.en.srt |
14.90KB |
| 053 Histograms part 2_ Number of bins.mp4 |
23.53MB |
| 054 Code_ Histogram bins.en.srt |
18.59KB |
| 054 Code_ Histogram bins.mp4 |
118.27MB |
| 055 Violin plots.en.srt |
5.19KB |
| 055 Violin plots.mp4 |
6.53MB |
| 056 Code_ violin plots.en.srt |
16.08KB |
| 056 Code_ violin plots.mp4 |
105.08MB |
| 057 _Unsupervised learning__ asymmetric violin plots.en.srt |
4.01KB |
| 057 _Unsupervised learning__ asymmetric violin plots.mp4 |
17.37MB |
| 058 Shannon entropy.en.srt |
16.14KB |
| 058 Shannon entropy.mp4 |
33.23MB |
| 059 Code_ entropy.en.srt |
31.58KB |
| 059 Code_ entropy.mp4 |
110.34MB |
| 060 _Unsupervised learning__ entropy and number of bins.en.srt |
2.09KB |
| 060 _Unsupervised learning__ entropy and number of bins.mp4 |
8.27MB |
| 061 Garbage in, garbage out (GIGO).en.srt |
5.90KB |
| 061 Garbage in, garbage out (GIGO).mp4 |
11.61MB |
| 062 Z-score standardization.en.srt |
14.89KB |
| 062 Z-score standardization.mp4 |
36.38MB |
| 063 Code_ z-score.en.srt |
20.05KB |
| 063 Code_ z-score.mp4 |
66.96MB |
| 064 Min-max scaling.en.srt |
7.52KB |
| 064 Min-max scaling.mp4 |
11.74MB |
| 065 Code_ min-max scaling.en.srt |
13.11KB |
| 065 Code_ min-max scaling.mp4 |
40.53MB |
| 066 _Unsupervised learning__ Invert the min-max scaling.en.srt |
3.77KB |
| 066 _Unsupervised learning__ Invert the min-max scaling.mp4 |
6.82MB |
| 067 What are outliers and why are they dangerous_.en.srt |
22.43KB |
| 067 What are outliers and why are they dangerous_.mp4 |
43.23MB |
| 068 Removing outliers_ z-score method.en.srt |
14.72KB |
| 068 Removing outliers_ z-score method.mp4 |
33.66MB |
| 069 The modified z-score method.en.srt |
6.13KB |
| 069 The modified z-score method.mp4 |
9.68MB |
| 070 Code_ z-score for outlier removal.en.srt |
35.06KB |
| 070 Code_ z-score for outlier removal.mp4 |
137.22MB |
| 071 _Unsupervised learning__ z vs. modified-z.en.srt |
3.99KB |
| 071 _Unsupervised learning__ z vs. modified-z.mp4 |
9.07MB |
| 072 Multivariate outlier detection.en.srt |
14.97KB |
| 072 Multivariate outlier detection.mp4 |
25.19MB |
| 073 Code_ Euclidean distance for outlier removal.en.srt |
13.29KB |
| 073 Code_ Euclidean distance for outlier removal.mp4 |
43.84MB |
| 074 Removing outliers by data trimming.en.srt |
8.87KB |
| 074 Removing outliers by data trimming.mp4 |
16.99MB |
| 075 Code_ Data trimming to remove outliers.en.srt |
16.97KB |
| 075 Code_ Data trimming to remove outliers.mp4 |
65.43MB |
| 076 Non-parametric solutions to outliers.en.srt |
6.58KB |
| 076 Non-parametric solutions to outliers.mp4 |
23.05MB |
| 077 An outlier lecture on personal accountability.en.srt |
4.28KB |
| 077 An outlier lecture on personal accountability.mp4 |
17.83MB |
| 078 What is probability_.en.srt |
18.66KB |
| 078 What is probability_.mp4 |
41.32MB |
| 079 Probability vs. proportion.en.srt |
14.74KB |
| 079 Probability vs. proportion.mp4 |
37.66MB |
| 080 Computing probabilities.en.srt |
15.77KB |
| 080 Computing probabilities.mp4 |
37.69MB |
| 081 Code_ compute probabilities.en.srt |
22.97KB |
| 081 Code_ compute probabilities.mp4 |
137.11MB |
| 082 Probability and odds.en.srt |
7.22KB |
| 082 Probability and odds.mp4 |
12.01MB |
| 083 _Unsupervised learning__ probabilities of odds-space.en.srt |
3.26KB |
| 083 _Unsupervised learning__ probabilities of odds-space.mp4 |
5.96MB |
| 084 Probability mass vs. density.en.srt |
19.16KB |
| 084 Probability mass vs. density.mp4 |
134.39MB |
| 085 Code_ compute probability mass functions.en.srt |
16.60KB |
| 085 Code_ compute probability mass functions.mp4 |
66.29MB |
| 086 Cumulative probability distributions.en.srt |
16.39KB |
| 086 Cumulative probability distributions.mp4 |
36.73MB |
| 087 Code_ cdfs and pdfs.en.srt |
14.71KB |
| 087 Code_ cdfs and pdfs.mp4 |
42.28MB |
| 088 _Unsupervised learning__ cdf's for various distributions.en.srt |
3.44KB |
| 088 _Unsupervised learning__ cdf's for various distributions.mp4 |
9.35MB |
| 089 Creating sample estimate distributions.en.srt |
28.87KB |
| 089 Creating sample estimate distributions.mp4 |
125.23MB |
| 090 Monte Carlo sampling.en.srt |
3.96KB |
| 090 Monte Carlo sampling.mp4 |
16.35MB |
| 091 Sampling variability, noise, and other annoyances.en.srt |
13.57KB |
| 091 Sampling variability, noise, and other annoyances.mp4 |
106.24MB |
| 092 Code_ sampling variability.en.srt |
39.81KB |
| 092 Code_ sampling variability.mp4 |
155.12MB |
| 093 Expected value.en.srt |
16.02KB |
| 093 Expected value.mp4 |
59.79MB |
| 094 Conditional probability.en.srt |
19.61KB |
| 094 Conditional probability.mp4 |
85.95MB |
| 095 Code_ conditional probabilities.en.srt |
30.85KB |
| 095 Code_ conditional probabilities.mp4 |
115.37MB |
| 096 Tree diagrams for conditional probabilities.en.srt |
10.34KB |
| 096 Tree diagrams for conditional probabilities.mp4 |
13.61MB |
| 097 The Law of Large Numbers.en.srt |
14.99KB |
| 097 The Law of Large Numbers.mp4 |
40.72MB |
| 098 Code_ Law of Large Numbers in action.en.srt |
29.00KB |
| 098 Code_ Law of Large Numbers in action.mp4 |
165.91MB |
| 099 The Central Limit Theorem.en.srt |
16.18KB |
| 099 The Central Limit Theorem.mp4 |
26.84MB |
| 100 Code_ the CLT in action.en.srt |
24.54KB |
| 100 Code_ the CLT in action.mp4 |
93.57MB |
| 101 _Unsupervised learning__ Averaging pairs of numbers.en.srt |
3.32KB |
| 101 _Unsupervised learning__ Averaging pairs of numbers.mp4 |
9.51MB |
| 102 IVs, DVs, models, and other stats lingo.en.srt |
25.29KB |
| 102 IVs, DVs, models, and other stats lingo.mp4 |
91.48MB |
| 103 What is an hypothesis and how do you specify one_.en.srt |
24.33KB |
| 103 What is an hypothesis and how do you specify one_.mp4 |
49.37MB |
| 104 Sample distributions under null and alternative hypotheses.en.srt |
15.25KB |
| 104 Sample distributions under null and alternative hypotheses.mp4 |
43.92MB |
| 105 P-values_ definition, tails, and misinterpretations.en.srt |
27.94KB |
| 105 P-values_ definition, tails, and misinterpretations.mp4 |
131.88MB |
| 106 P-z combinations that you should memorize.en.srt |
9.39KB |
| 106 P-z combinations that you should memorize.mp4 |
17.33MB |
| 107 Degrees of freedom.en.srt |
19.38KB |
| 107 Degrees of freedom.mp4 |
33.10MB |
| 108 Type 1 and Type 2 errors.en.srt |
23.14KB |
| 108 Type 1 and Type 2 errors.mp4 |
46.14MB |
| 109 Parametric vs. non-parametric tests.en.srt |
13.35KB |
| 109 Parametric vs. non-parametric tests.mp4 |
87.66MB |
| 110 Multiple comparisons and Bonferroni correction.en.srt |
13.01KB |
| 110 Multiple comparisons and Bonferroni correction.mp4 |
29.70MB |
| 111 Statistical vs. theoretical vs. clinical significance.en.srt |
10.39KB |
| 111 Statistical vs. theoretical vs. clinical significance.mp4 |
19.19MB |
| 112 Cross-validation.en.srt |
17.07KB |
| 112 Cross-validation.mp4 |
28.44MB |
| 113 Statistical significance vs. classification accuracy.en.srt |
17.72KB |
| 113 Statistical significance vs. classification accuracy.mp4 |
42.69MB |
| 114 Purpose and interpretation of the t-test.en.srt |
19.67KB |
| 114 Purpose and interpretation of the t-test.mp4 |
32.21MB |
| 115 One-sample t-test.en.srt |
12.06KB |
| 115 One-sample t-test.mp4 |
54.10MB |
| 116 Code_ One-sample t-test.en.srt |
32.59KB |
| 116 Code_ One-sample t-test.mp4 |
158.23MB |
| 117 _Unsupervised learning__ The role of variance.en.srt |
4.28KB |
| 117 _Unsupervised learning__ The role of variance.mp4 |
28.68MB |
| 118 Two-samples t-test.en.srt |
19.73KB |
| 118 Two-samples t-test.mp4 |
93.81MB |
| 119 Code_ Two-samples t-test.en.srt |
33.52KB |
| 119 Code_ Two-samples t-test.mp4 |
211.61MB |
| 120 _Unsupervised learning__ Importance of N for t-test.en.srt |
7.14KB |
| 120 _Unsupervised learning__ Importance of N for t-test.mp4 |
20.09MB |
| 121 Wilcoxon signed-rank (nonparametric t-test).en.srt |
10.84KB |
| 121 Wilcoxon signed-rank (nonparametric t-test).mp4 |
30.44MB |
| 122 Code_ Signed-rank test.en.srt |
28.04KB |
| 122 Code_ Signed-rank test.mp4 |
162.12MB |
| 123 Mann-Whitney U test (nonparametric t-test).en.srt |
9.20KB |
| 123 Mann-Whitney U test (nonparametric t-test).mp4 |
20.41MB |
| 124 Code_ Mann-Whitney U test.en.srt |
8.07KB |
| 124 Code_ Mann-Whitney U test.mp4 |
52.05MB |
| 125 Permutation testing for t-test significance.en.srt |
17.00KB |
| 125 Permutation testing for t-test significance.mp4 |
63.66MB |
| 126 Code_ permutation testing.en.srt |
38.65KB |
| 126 Code_ permutation testing.mp4 |
241.29MB |
| 127 _Unsupervised learning__ How many permutations_.en.srt |
8.05KB |
| 127 _Unsupervised learning__ How many permutations_.mp4 |
55.40MB |
| 128 What are confidence intervals and why do we need them_.en.srt |
13.66KB |
| 128 What are confidence intervals and why do we need them_.mp4 |
29.97MB |
| 129 Computing confidence intervals via formula.en.srt |
10.30KB |
| 129 Computing confidence intervals via formula.mp4 |
17.44MB |
| 130 Code_ compute confidence intervals by formula.en.srt |
26.75KB |
| 130 Code_ compute confidence intervals by formula.mp4 |
149.63MB |
| 131 Confidence intervals via bootstrapping (resampling).en.srt |
13.33KB |
| 131 Confidence intervals via bootstrapping (resampling).mp4 |
54.41MB |
| 132 Code_ bootstrapping confidence intervals.en.srt |
22.62KB |
| 132 Code_ bootstrapping confidence intervals.mp4 |
136.76MB |
| 133 _Unsupervised learning__ Confidence intervals for variance.en.srt |
1.96KB |
| 133 _Unsupervised learning__ Confidence intervals for variance.mp4 |
8.57MB |
| 134 Misconceptions about confidence intervals.en.srt |
9.45KB |
| 134 Misconceptions about confidence intervals.mp4 |
18.70MB |
| 135 Motivation and description of correlation.en.srt |
28.49KB |
| 135 Motivation and description of correlation.mp4 |
96.65MB |
| 136 Covariance and correlation_ formulas.en.srt |
21.67KB |
| 136 Covariance and correlation_ formulas.mp4 |
42.08MB |
| 137 Code_ correlation coefficient.en.srt |
42.12KB |
| 137 Code_ correlation coefficient.mp4 |
214.65MB |
| 138 Code_ Simulate data with specified correlation.en.srt |
20.84KB |
| 138 Code_ Simulate data with specified correlation.mp4 |
136.21MB |
| 139 Correlation matrix.en.srt |
14.17KB |
| 139 Correlation matrix.mp4 |
31.12MB |
| 140 Code_ correlation matrix.en.srt |
33.24KB |
| 140 Code_ correlation matrix.mp4 |
282.79MB |
| 141 _Unsupervised learning__ average correlation matrices.en.srt |
4.23KB |
| 141 _Unsupervised learning__ average correlation matrices.mp4 |
18.53MB |
| 142 _Unsupervised learning__ correlation to covariance matrix.en.srt |
6.03KB |
| 142 _Unsupervised learning__ correlation to covariance matrix.mp4 |
10.20MB |
| 143 Partial correlation.en.srt |
16.04KB |
| 143 Partial correlation.mp4 |
59.54MB |
| 144 Code_ partial correlation.en.srt |
30.63KB |
| 144 Code_ partial correlation.mp4 |
108.26MB |
| 145 The problem with Pearson.en.srt |
10.28KB |
| 145 The problem with Pearson.mp4 |
16.69MB |
| 146 Nonparametric correlation_ Spearman rank.en.srt |
11.17KB |
| 146 Nonparametric correlation_ Spearman rank.mp4 |
23.84MB |
| 147 Fisher-Z transformation for correlations.en.srt |
10.25KB |
| 147 Fisher-Z transformation for correlations.mp4 |
28.60MB |
| 148 Code_ Spearman correlation and Fisher-Z.en.srt |
11.55KB |
| 148 Code_ Spearman correlation and Fisher-Z.mp4 |
42.81MB |
| 149 _Unsupervised learning__ Spearman correlation.en.srt |
1.92KB |
| 149 _Unsupervised learning__ Spearman correlation.mp4 |
15.96MB |
| 150 _Unsupervised learning__ confidence interval on correlation.en.srt |
3.44KB |
| 150 _Unsupervised learning__ confidence interval on correlation.mp4 |
8.90MB |
| 151 Kendall's correlation for ordinal data.en.srt |
15.85KB |
| 151 Kendall's correlation for ordinal data.mp4 |
30.32MB |
| 152 Code_ Kendall correlation.en.srt |
27.92KB |
| 152 Code_ Kendall correlation.mp4 |
184.47MB |
| 153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.en.srt |
3.50KB |
| 153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 |
14.95MB |
| 154 Cosine similarity.en.srt |
7.78KB |
| 154 Cosine similarity.mp4 |
14.28MB |
| 155 Code_ Cosine similarity vs. Pearson correlation.en.srt |
32.54KB |
| 155 Code_ Cosine similarity vs. Pearson correlation.mp4 |
102.53MB |
| 156 ANOVA intro, part1.en.srt |
27.24KB |
| 156 ANOVA intro, part1.mp4 |
137.94MB |
| 157 ANOVA intro, part 2.en.srt |
29.58KB |
| 157 ANOVA intro, part 2.mp4 |
84.60MB |
| 158 Sum of squares.en.srt |
26.56KB |
| 158 Sum of squares.mp4 |
46.02MB |
| 159 The F-test and the ANOVA table.en.srt |
10.87KB |
| 159 The F-test and the ANOVA table.mp4 |
20.02MB |
| 160 The omnibus F-test and post-hoc comparisons.en.srt |
19.62KB |
| 160 The omnibus F-test and post-hoc comparisons.mp4 |
63.61MB |
| 161 The two-way ANOVA.en.srt |
30.60KB |
| 161 The two-way ANOVA.mp4 |
104.77MB |
| 162 One-way ANOVA example.en.srt |
21.47KB |
| 162 One-way ANOVA example.mp4 |
44.53MB |
| 163 Code_ One-way ANOVA (independent samples).en.srt |
26.84KB |
| 163 Code_ One-way ANOVA (independent samples).mp4 |
172.94MB |
| 164 Code_ One-way repeated-measures ANOVA.en.srt |
19.13KB |
| 164 Code_ One-way repeated-measures ANOVA.mp4 |
73.30MB |
| 165 Two-way ANOVA example.en.srt |
17.38KB |
| 165 Two-way ANOVA example.mp4 |
35.83MB |
| 166 Code_ Two-way mixed ANOVA.en.srt |
22.35KB |
| 166 Code_ Two-way mixed ANOVA.mp4 |
114.36MB |
| 167 Introduction to GLM _ regression.en.srt |
30.97KB |
| 167 Introduction to GLM _ regression.mp4 |
62.31MB |
| 168 Least-squares solution to the GLM.en.srt |
14.92KB |
| 168 Least-squares solution to the GLM.mp4 |
41.59MB |
| 169 Evaluating regression models_ R2 and F.en.srt |
24.80KB |
| 169 Evaluating regression models_ R2 and F.mp4 |
38.33MB |
| 170 Simple regression.en.srt |
20.52KB |
| 170 Simple regression.mp4 |
36.98MB |
| 171 Code_ simple regression.en.srt |
13.99KB |
| 171 Code_ simple regression.mp4 |
52.36MB |
| 172 _Unsupervised learning__ Compute R2 and F.en.srt |
1.50KB |
| 172 _Unsupervised learning__ Compute R2 and F.mp4 |
4.70MB |
| 173 Multiple regression.en.srt |
19.91KB |
| 173 Multiple regression.mp4 |
69.08MB |
| 174 Standardizing regression coefficients.en.srt |
19.11KB |
| 174 Standardizing regression coefficients.mp4 |
47.47MB |
| 175 Code_ Multiple regression.en.srt |
29.08KB |
| 175 Code_ Multiple regression.mp4 |
171.33MB |
| 176 Polynomial regression models.en.srt |
13.98KB |
| 176 Polynomial regression models.mp4 |
49.20MB |
| 177 Code_ polynomial modeling.en.srt |
23.36KB |
| 177 Code_ polynomial modeling.mp4 |
129.33MB |
| 178 _Unsupervised learning__ Polynomial design matrix.en.srt |
1.15KB |
| 178 _Unsupervised learning__ Polynomial design matrix.mp4 |
5.47MB |
| 179 Logistic regression.en.srt |
26.53KB |
| 179 Logistic regression.mp4 |
52.98MB |
| 180 Code_ Logistic regression.en.srt |
14.79KB |
| 180 Code_ Logistic regression.mp4 |
81.40MB |
| 181 Under- and over-fitting.en.srt |
26.45KB |
| 181 Under- and over-fitting.mp4 |
121.15MB |
| 182 _Unsupervised learning__ Overfit data.en.srt |
2.79KB |
| 182 _Unsupervised learning__ Overfit data.mp4 |
4.85MB |
| 183 Comparing _nested_ models.en.srt |
19.08KB |
| 183 Comparing _nested_ models.mp4 |
39.30MB |
| 184 What to do about missing data.en.srt |
9.97KB |
| 184 What to do about missing data.mp4 |
16.15MB |
| 185 What is statistical power and why is it important_.en.srt |
14.88KB |
| 185 What is statistical power and why is it important_.mp4 |
39.69MB |
| 186 Estimating statistical power and sample size.en.srt |
17.24KB |
| 186 Estimating statistical power and sample size.mp4 |
31.07MB |
| 187 Compute power and sample size using G_Power.en.srt |
7.14KB |
| 187 Compute power and sample size using G_Power.mp4 |
31.24MB |
| 188 K-means clustering.en.srt |
21.87KB |
| 188 K-means clustering.mp4 |
54.51MB |
| 189 Code_ k-means clustering.en.srt |
35.78KB |
| 189 Code_ k-means clustering.mp4 |
230.73MB |
| 190 _Unsupervised learning__ K-means and normalization.en.srt |
2.57KB |
| 190 _Unsupervised learning__ K-means and normalization.mp4 |
11.21MB |
| 191 _Unsupervised learning__ K-means on a Gauss blur.en.srt |
2.08KB |
| 191 _Unsupervised learning__ K-means on a Gauss blur.mp4 |
7.94MB |
| 192 Clustering via dbscan.en.srt |
22.56KB |
| 192 Clustering via dbscan.mp4 |
100.70MB |
| 193 Code_ dbscan.en.srt |
51.46KB |
| 193 Code_ dbscan.mp4 |
288.67MB |
| 194 _Unsupervised learning__ dbscan vs. k-means.en.srt |
4.61KB |
| 194 _Unsupervised learning__ dbscan vs. k-means.mp4 |
20.00MB |
| 195 K-nearest neighbor classification.en.srt |
9.35KB |
| 195 K-nearest neighbor classification.mp4 |
12.57MB |
| 196 Code_ KNN.en.srt |
19.02KB |
| 196 Code_ KNN.mp4 |
108.60MB |
| 197 Principal components analysis (PCA).en.srt |
24.17KB |
| 197 Principal components analysis (PCA).mp4 |
42.83MB |
| 198 Code_ PCA.en.srt |
27.67KB |
| 198 Code_ PCA.mp4 |
73.10MB |
| 199 _Unsupervised learning__ K-means on PC data.en.srt |
2.30KB |
| 199 _Unsupervised learning__ K-means on PC data.mp4 |
11.60MB |
| 200 Independent components analysis (ICA).en.srt |
17.90KB |
| 200 Independent components analysis (ICA).mp4 |
45.70MB |
| 201 Code_ ICA.en.srt |
19.20KB |
| 201 Code_ ICA.mp4 |
73.53MB |
| 202 The two perspectives of the world.en.srt |
9.08KB |
| 202 The two perspectives of the world.mp4 |
14.00MB |
| 203 d-prime.en.srt |
20.03KB |
| 203 d-prime.mp4 |
39.59MB |
| 204 Code_ d-prime.en.srt |
22.76KB |
| 204 Code_ d-prime.mp4 |
69.75MB |
| 205 Response bias.en.srt |
12.77KB |
| 205 Response bias.mp4 |
21.95MB |
| 206 Code_ Response bias.en.srt |
6.61KB |
| 206 Code_ Response bias.mp4 |
22.90MB |
| 207 Receiver operating characteristics (ROC).en.srt |
11.38KB |
| 207 Receiver operating characteristics (ROC).mp4 |
64.45MB |
| 208 Code_ ROC curves.en.srt |
12.13KB |
| 208 Code_ ROC curves.mp4 |
54.76MB |
| 209 _Unsupervised learning__ Make this plot look nicer!.en.srt |
2.44KB |
| 209 _Unsupervised learning__ Make this plot look nicer!.mp4 |
11.54MB |
| 210 About deep learning.html |
1.79KB |
| 211 Bonus content.html |
4.21KB |