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

收录时间:2021-05-10 11:18:11
文件大小:12GB
下载次数:1
最近下载:2021-05-10 11:18:11
磁力链接:
magnet:?xt=urn:btih:f9dc0101ca8d990a05ee3f012e4058f0b5c94fbb

立即下载 复制链接

文件列表

06 Descriptive statistics/039 Code_ data from different distributions.mp4 304MB
16 Clustering and dimension-reduction/193 Code_ dbscan.mp4 289MB
12 Correlation/140 Code_ correlation matrix.mp4 283MB
06 Descriptive statistics/047 Code_ Computing dispersion.mp4 267MB
10 The t-test family/126 Code_ permutation testing.mp4 241MB
16 Clustering and dimension-reduction/189 Code_ k-means clustering.mp4 231MB
12 Correlation/137 Code_ correlation coefficient.mp4 215MB
10 The t-test family/119 Code_ Two-samples t-test.mp4 212MB
12 Correlation/152 Code_ Kendall correlation.mp4 184MB
13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).mp4 173MB
14 Regression/175 Code_ Multiple regression.mp4 171MB
08 Probability theory/098 Code_ Law of Large Numbers in action.mp4 166MB
10 The t-test family/122 Code_ Signed-rank test.mp4 162MB
10 The t-test family/116 Code_ One-sample t-test.mp4 158MB
08 Probability theory/092 Code_ sampling variability.mp4 155MB
11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.mp4 150MB
13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.mp4 138MB
07 Data normalizations and outliers/070 Code_ z-score for outlier removal.mp4 137MB
08 Probability theory/081 Code_ compute probabilities.mp4 137MB
11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.mp4 137MB
12 Correlation/138 Code_ Simulate data with specified correlation.mp4 136MB
08 Probability theory/084 Probability mass vs. density.mp4 134MB
05 Visualizing data/028 Code_ histograms.mp4 134MB
09 Hypothesis testing/105 P-values_ definition, tails, and misinterpretations.mp4 132MB
14 Regression/177 Code_ polynomial modeling.mp4 129MB
08 Probability theory/089 Creating sample estimate distributions.mp4 125MB
14 Regression/181 Under- and over-fitting.mp4 121MB
06 Descriptive statistics/054 Code_ Histogram bins.mp4 118MB
08 Probability theory/095 Code_ conditional probabilities.mp4 115MB
13 Analysis of Variance (ANOVA)/166 Code_ Two-way mixed ANOVA.mp4 114MB
06 Descriptive statistics/059 Code_ entropy.mp4 110MB
16 Clustering and dimension-reduction/196 Code_ KNN.mp4 109MB
12 Correlation/144 Code_ partial correlation.mp4 108MB
08 Probability theory/091 Sampling variability, noise, and other annoyances.mp4 106MB
06 Descriptive statistics/056 Code_ violin plots.mp4 105MB
13 Analysis of Variance (ANOVA)/161 The two-way ANOVA.mp4 105MB
12 Correlation/155 Code_ Cosine similarity vs. Pearson correlation.mp4 103MB
16 Clustering and dimension-reduction/192 Clustering via dbscan.mp4 101MB
05 Visualizing data/023 Code_ bar plots.mp4 100MB
12 Correlation/135 Motivation and description of correlation.mp4 97MB
10 The t-test family/118 Two-samples t-test.mp4 94MB
08 Probability theory/100 Code_ the CLT in action.mp4 94MB
09 Hypothesis testing/102 IVs, DVs, models, and other stats lingo.mp4 91MB
06 Descriptive statistics/051 Code_ QQ plots.mp4 91MB
09 Hypothesis testing/109 Parametric vs. non-parametric tests.mp4 88MB
08 Probability theory/094 Conditional probability.mp4 86MB
13 Analysis of Variance (ANOVA)/157 ANOVA intro, part 2.mp4 85MB
05 Visualizing data/025 Code_ box plots.mp4 84MB
06 Descriptive statistics/049 Code_ IQR.mp4 84MB
14 Regression/180 Code_ Logistic regression.mp4 81MB
01 Introductions/003 Statistics guessing game!.mp4 80MB
06 Descriptive statistics/044 Code_ computing central tendency.mp4 76MB
16 Clustering and dimension-reduction/201 Code_ ICA.mp4 74MB
13 Analysis of Variance (ANOVA)/164 Code_ One-way repeated-measures ANOVA.mp4 73MB
16 Clustering and dimension-reduction/198 Code_ PCA.mp4 73MB
17 Signal detection theory/204 Code_ d-prime.mp4 70MB
05 Visualizing data/031 Code_ pie charts.mp4 69MB
14 Regression/173 Multiple regression.mp4 69MB
07 Data normalizations and outliers/063 Code_ z-score.mp4 67MB
08 Probability theory/085 Code_ compute probability mass functions.mp4 66MB
07 Data normalizations and outliers/075 Code_ Data trimming to remove outliers.mp4 65MB
17 Signal detection theory/207 Receiver operating characteristics (ROC).mp4 64MB
10 The t-test family/125 Permutation testing for t-test significance.mp4 64MB
13 Analysis of Variance (ANOVA)/160 The omnibus F-test and post-hoc comparisons.mp4 64MB
14 Regression/167 Introduction to GLM _ regression.mp4 62MB
08 Probability theory/093 Expected value.mp4 60MB
04 What are (is_) data_/017 Types of data_ categorical, numerical, etc.mp4 60MB
12 Correlation/143 Partial correlation.mp4 60MB
10 The t-test family/127 _Unsupervised learning__ How many permutations_.mp4 55MB
17 Signal detection theory/208 Code_ ROC curves.mp4 55MB
16 Clustering and dimension-reduction/188 K-means clustering.mp4 55MB
06 Descriptive statistics/046 Measures of dispersion (variance, standard deviation).mp4 54MB
11 Confidence intervals on parameters/131 Confidence intervals via bootstrapping (resampling).mp4 54MB
10 The t-test family/115 One-sample t-test.mp4 54MB
14 Regression/179 Logistic regression.mp4 53MB
14 Regression/171 Code_ simple regression.mp4 52MB
10 The t-test family/124 Code_ Mann-Whitney U test.mp4 52MB
09 Hypothesis testing/103 What is an hypothesis and how do you specify one_.mp4 49MB
14 Regression/176 Polynomial regression models.mp4 49MB
04 What are (is_) data_/018 Code_ representing types of data on computers.mp4 48MB
14 Regression/174 Standardizing regression coefficients.mp4 47MB
09 Hypothesis testing/108 Type 1 and Type 2 errors.mp4 46MB
13 Analysis of Variance (ANOVA)/158 Sum of squares.mp4 46MB
16 Clustering and dimension-reduction/200 Independent components analysis (ICA).mp4 46MB
13 Analysis of Variance (ANOVA)/162 One-way ANOVA example.mp4 45MB
09 Hypothesis testing/104 Sample distributions under null and alternative hypotheses.mp4 44MB
05 Visualizing data/027 Histograms.mp4 44MB
07 Data normalizations and outliers/073 Code_ Euclidean distance for outlier removal.mp4 44MB
07 Data normalizations and outliers/067 What are outliers and why are they dangerous_.mp4 43MB
16 Clustering and dimension-reduction/197 Principal components analysis (PCA).mp4 43MB
12 Correlation/148 Code_ Spearman correlation and Fisher-Z.mp4 43MB
09 Hypothesis testing/113 Statistical significance vs. classification accuracy.mp4 43MB
08 Probability theory/087 Code_ cdfs and pdfs.mp4 42MB
12 Correlation/136 Covariance and correlation_ formulas.mp4 42MB
14 Regression/168 Least-squares solution to the GLM.mp4 42MB
08 Probability theory/078 What is probability_.mp4 41MB
08 Probability theory/097 The Law of Large Numbers.mp4 41MB
07 Data normalizations and outliers/065 Code_ min-max scaling.mp4 41MB
15 Statistical power and sample sizes/185 What is statistical power and why is it important_.mp4 40MB
17 Signal detection theory/203 d-prime.mp4 40MB
14 Regression/183 Comparing _nested_ models.mp4 39MB
06 Descriptive statistics/042 Measures of central tendency (mean).mp4 39MB
01 Introductions/002 About using MATLAB or Python.mp4 39MB
14 Regression/169 Evaluating regression models_ R2 and F.mp4 38MB
01 Introductions/001 [Important] Getting the most out of this course.mp4 38MB
08 Probability theory/080 Computing probabilities.mp4 38MB
08 Probability theory/079 Probability vs. proportion.mp4 38MB
05 Visualizing data/034 Code_ line plots.mp4 37MB
04 What are (is_) data_/019 Sample vs. population data.mp4 37MB
05 Visualizing data/022 Bar plots.mp4 37MB
14 Regression/170 Simple regression.mp4 37MB
08 Probability theory/086 Cumulative probability distributions.mp4 37MB
07 Data normalizations and outliers/062 Z-score standardization.mp4 36MB
13 Analysis of Variance (ANOVA)/165 Two-way ANOVA example.mp4 36MB
04 What are (is_) data_/016 Where do data come from and what do they mean_.mp4 36MB
06 Descriptive statistics/043 Measures of central tendency (median, mode).mp4 34MB
07 Data normalizations and outliers/068 Removing outliers_ z-score method.mp4 34MB
06 Descriptive statistics/058 Shannon entropy.mp4 33MB
09 Hypothesis testing/107 Degrees of freedom.mp4 33MB
10 The t-test family/114 Purpose and interpretation of the t-test.mp4 32MB
06 Descriptive statistics/038 Data distributions.mp4 32MB
15 Statistical power and sample sizes/187 Compute power and sample size using G_Power.mp4 31MB
12 Correlation/139 Correlation matrix.mp4 31MB
15 Statistical power and sample sizes/186 Estimating statistical power and sample size.mp4 31MB
10 The t-test family/121 Wilcoxon signed-rank (nonparametric t-test).mp4 30MB
12 Correlation/151 Kendall's correlation for ordinal data.mp4 30MB
11 Confidence intervals on parameters/128 What are confidence intervals and why do we need them_.mp4 30MB
09 Hypothesis testing/110 Multiple comparisons and Bonferroni correction.mp4 30MB
10 The t-test family/117 _Unsupervised learning__ The role of variance.mp4 29MB
12 Correlation/147 Fisher-Z transformation for correlations.mp4 29MB
09 Hypothesis testing/112 Cross-validation.mp4 28MB
02 Math prerequisites/006 Should you memorize statistical formulas_.mp4 28MB
08 Probability theory/099 The Central Limit Theorem.mp4 27MB
05 Visualizing data/033 Linear vs. logarithmic axis scaling.mp4 26MB
06 Descriptive statistics/037 Accuracy, precision, resolution.mp4 26MB
07 Data normalizations and outliers/072 Multivariate outlier detection.mp4 25MB
01 Introductions/004 Using the Q&A forum.mp4 24MB
12 Correlation/146 Nonparametric correlation_ Spearman rank.mp4 24MB
06 Descriptive statistics/053 Histograms part 2_ Number of bins.mp4 24MB
07 Data normalizations and outliers/076 Non-parametric solutions to outliers.mp4 23MB
17 Signal detection theory/206 Code_ Response bias.mp4 23MB
17 Signal detection theory/205 Response bias.mp4 22MB
06 Descriptive statistics/052 Statistical _moments_.mp4 22MB
06 Descriptive statistics/036 Descriptive vs. inferential statistics.mp4 22MB
10 The t-test family/123 Mann-Whitney U test (nonparametric t-test).mp4 20MB
10 The t-test family/120 _Unsupervised learning__ Importance of N for t-test.mp4 20MB
13 Analysis of Variance (ANOVA)/159 The F-test and the ANOVA table.mp4 20MB
16 Clustering and dimension-reduction/194 _Unsupervised learning__ dbscan vs. k-means.mp4 20MB
04 What are (is_) data_/021 The ethics of making up data.mp4 20MB
09 Hypothesis testing/111 Statistical vs. theoretical vs. clinical significance.mp4 19MB
11 Confidence intervals on parameters/134 Misconceptions about confidence intervals.mp4 19MB
12 Correlation/141 _Unsupervised learning__ average correlation matrices.mp4 19MB
05 Visualizing data/032 When to use lines instead of bars.mp4 18MB
02 Math prerequisites/012 The logistic function.mp4 18MB
04 What are (is_) data_/020 Samples, case reports, and anecdotes.mp4 18MB
07 Data normalizations and outliers/077 An outlier lecture on personal accountability.mp4 18MB
11 Confidence intervals on parameters/129 Computing confidence intervals via formula.mp4 17MB
06 Descriptive statistics/057 _Unsupervised learning__ asymmetric violin plots.mp4 17MB
09 Hypothesis testing/106 P-z combinations that you should memorize.mp4 17MB
07 Data normalizations and outliers/074 Removing outliers by data trimming.mp4 17MB
06 Descriptive statistics/045 _Unsupervised learning__ central tendencies with outliers.mp4 17MB
12 Correlation/145 The problem with Pearson.mp4 17MB
05 Visualizing data/030 Pie charts.mp4 17MB
08 Probability theory/090 Monte Carlo sampling.mp4 16MB
06 Descriptive statistics/050 QQ plots.mp4 16MB
14 Regression/184 What to do about missing data.mp4 16MB
12 Correlation/149 _Unsupervised learning__ Spearman correlation.mp4 16MB
12 Correlation/153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 15MB
03 IMPORTANT_ Download course materials/014 Download materials for the entire course!.mp4 15MB
12 Correlation/154 Cosine similarity.mp4 14MB
17 Signal detection theory/202 The two perspectives of the world.mp4 14MB
08 Probability theory/096 Tree diagrams for conditional probabilities.mp4 14MB
02 Math prerequisites/008 Scientific notation.mp4 13MB
02 Math prerequisites/013 Rank and tied-rank.mp4 13MB
16 Clustering and dimension-reduction/195 K-nearest neighbor classification.mp4 13MB
02 Math prerequisites/011 Natural exponent and logarithm.mp4 12MB
08 Probability theory/082 Probability and odds.mp4 12MB
05 Visualizing data/029 _Unsupervised learning__ Histogram proportion.mp4 12MB
07 Data normalizations and outliers/064 Min-max scaling.mp4 12MB
07 Data normalizations and outliers/061 Garbage in, garbage out (GIGO).mp4 12MB
16 Clustering and dimension-reduction/199 _Unsupervised learning__ K-means on PC data.mp4 12MB
17 Signal detection theory/209 _Unsupervised learning__ Make this plot look nicer!.mp4 12MB
16 Clustering and dimension-reduction/190 _Unsupervised learning__ K-means and normalization.mp4 11MB
05 Visualizing data/024 Box-and-whisker plots.mp4 11MB
04 What are (is_) data_/015 Is _data_ singular or plural_!_!!_!.mp4 11MB
06 Descriptive statistics/041 The beauty and simplicity of Normal.mp4 10MB
06 Descriptive statistics/040 _Unsupervised learning__ histograms of distributions.mp4 10MB
12 Correlation/142 _Unsupervised learning__ correlation to covariance matrix.mp4 10MB
06 Descriptive statistics/048 Interquartile range (IQR).mp4 10MB
07 Data normalizations and outliers/069 The modified z-score method.mp4 10MB
08 Probability theory/101 _Unsupervised learning__ Averaging pairs of numbers.mp4 10MB
08 Probability theory/088 _Unsupervised learning__ cdf's for various distributions.mp4 9MB
07 Data normalizations and outliers/071 _Unsupervised learning__ z vs. modified-z.mp4 9MB
12 Correlation/150 _Unsupervised learning__ confidence interval on correlation.mp4 9MB
11 Confidence intervals on parameters/133 _Unsupervised learning__ Confidence intervals for variance.mp4 9MB
01 Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.mp4 8MB
06 Descriptive statistics/060 _Unsupervised learning__ entropy and number of bins.mp4 8MB
05 Visualizing data/026 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4 8MB
16 Clustering and dimension-reduction/191 _Unsupervised learning__ K-means on a Gauss blur.mp4 8MB
02 Math prerequisites/009 Summation notation.mp4 8MB
02 Math prerequisites/007 Arithmetic and exponents.mp4 8MB
02 Math prerequisites/010 Absolute value.mp4 7MB
07 Data normalizations and outliers/066 _Unsupervised learning__ Invert the min-max scaling.mp4 7MB
06 Descriptive statistics/055 Violin plots.mp4 7MB
08 Probability theory/083 _Unsupervised learning__ probabilities of odds-space.mp4 6MB
14 Regression/178 _Unsupervised learning__ Polynomial design matrix.mp4 5MB
14 Regression/182 _Unsupervised learning__ Overfit data.mp4 5MB
14 Regression/172 _Unsupervised learning__ Compute R2 and F.mp4 5MB
05 Visualizing data/035 _Unsupervised learning__ log-scaled plots.mp4 4MB
03 IMPORTANT_ Download course materials/014 statsML.zip 1MB
16 Clustering and dimension-reduction/193 Code_ dbscan.en.srt 51KB
06 Descriptive statistics/039 Code_ data from different distributions.en.srt 48KB
12 Correlation/137 Code_ correlation coefficient.en.srt 42KB
08 Probability theory/092 Code_ sampling variability.en.srt 40KB
06 Descriptive statistics/047 Code_ Computing dispersion.en.srt 39KB
10 The t-test family/126 Code_ permutation testing.en.srt 39KB
16 Clustering and dimension-reduction/189 Code_ k-means clustering.en.srt 36KB
07 Data normalizations and outliers/070 Code_ z-score for outlier removal.en.srt 35KB
10 The t-test family/119 Code_ Two-samples t-test.en.srt 34KB
12 Correlation/140 Code_ correlation matrix.en.srt 33KB
10 The t-test family/116 Code_ One-sample t-test.en.srt 33KB
12 Correlation/155 Code_ Cosine similarity vs. Pearson correlation.en.srt 33KB
06 Descriptive statistics/059 Code_ entropy.en.srt 32KB
14 Regression/167 Introduction to GLM _ regression.en.srt 31KB
08 Probability theory/095 Code_ conditional probabilities.en.srt 31KB
12 Correlation/144 Code_ partial correlation.en.srt 31KB
13 Analysis of Variance (ANOVA)/161 The two-way ANOVA.en.srt 31KB
13 Analysis of Variance (ANOVA)/157 ANOVA intro, part 2.en.srt 30KB
14 Regression/175 Code_ Multiple regression.en.srt 29KB
08 Probability theory/098 Code_ Law of Large Numbers in action.en.srt 29KB
08 Probability theory/089 Creating sample estimate distributions.en.srt 29KB
12 Correlation/135 Motivation and description of correlation.en.srt 28KB
10 The t-test family/122 Code_ Signed-rank test.en.srt 28KB
09 Hypothesis testing/105 P-values_ definition, tails, and misinterpretations.en.srt 28KB
12 Correlation/152 Code_ Kendall correlation.en.srt 28KB
16 Clustering and dimension-reduction/198 Code_ PCA.en.srt 28KB
06 Descriptive statistics/046 Measures of dispersion (variance, standard deviation).en.srt 27KB
13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.en.srt 27KB
13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).en.srt 27KB
11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.en.srt 27KB
13 Analysis of Variance (ANOVA)/158 Sum of squares.en.srt 27KB
14 Regression/179 Logistic regression.en.srt 27KB
05 Visualizing data/023 Code_ bar plots.en.srt 26KB
14 Regression/181 Under- and over-fitting.en.srt 26KB
09 Hypothesis testing/102 IVs, DVs, models, and other stats lingo.en.srt 25KB
05 Visualizing data/028 Code_ histograms.en.srt 25KB
14 Regression/169 Evaluating regression models_ R2 and F.en.srt 25KB
08 Probability theory/100 Code_ the CLT in action.en.srt 25KB
06 Descriptive statistics/051 Code_ QQ plots.en.srt 24KB
06 Descriptive statistics/049 Code_ IQR.en.srt 24KB
09 Hypothesis testing/103 What is an hypothesis and how do you specify one_.en.srt 24KB
16 Clustering and dimension-reduction/197 Principal components analysis (PCA).en.srt 24KB
14 Regression/177 Code_ polynomial modeling.en.srt 23KB
09 Hypothesis testing/108 Type 1 and Type 2 errors.en.srt 23KB
08 Probability theory/081 Code_ compute probabilities.en.srt 23KB
17 Signal detection theory/204 Code_ d-prime.en.srt 23KB
11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.en.srt 23KB
16 Clustering and dimension-reduction/192 Clustering via dbscan.en.srt 23KB
07 Data normalizations and outliers/067 What are outliers and why are they dangerous_.en.srt 22KB
13 Analysis of Variance (ANOVA)/166 Code_ Two-way mixed ANOVA.en.srt 22KB
16 Clustering and dimension-reduction/188 K-means clustering.en.srt 22KB
04 What are (is_) data_/017 Types of data_ categorical, numerical, etc.en.srt 22KB
12 Correlation/136 Covariance and correlation_ formulas.en.srt 22KB
13 Analysis of Variance (ANOVA)/162 One-way ANOVA example.en.srt 21KB
06 Descriptive statistics/044 Code_ computing central tendency.en.srt 21KB
12 Correlation/138 Code_ Simulate data with specified correlation.en.srt 21KB
14 Regression/170 Simple regression.en.srt 21KB
05 Visualizing data/031 Code_ pie charts.en.srt 20KB
07 Data normalizations and outliers/063 Code_ z-score.en.srt 20KB
17 Signal detection theory/203 d-prime.en.srt 20KB
14 Regression/173 Multiple regression.en.srt 20KB
06 Descriptive statistics/042 Measures of central tendency (mean).en.srt 20KB
10 The t-test family/118 Two-samples t-test.en.srt 20KB
10 The t-test family/114 Purpose and interpretation of the t-test.en.srt 20KB
13 Analysis of Variance (ANOVA)/160 The omnibus F-test and post-hoc comparisons.en.srt 20KB
08 Probability theory/094 Conditional probability.en.srt 20KB
09 Hypothesis testing/107 Degrees of freedom.en.srt 19KB
16 Clustering and dimension-reduction/201 Code_ ICA.en.srt 19KB
08 Probability theory/084 Probability mass vs. density.en.srt 19KB
13 Analysis of Variance (ANOVA)/164 Code_ One-way repeated-measures ANOVA.en.srt 19KB
14 Regression/174 Standardizing regression coefficients.en.srt 19KB
14 Regression/183 Comparing _nested_ models.en.srt 19KB
16 Clustering and dimension-reduction/196 Code_ KNN.en.srt 19KB
06 Descriptive statistics/043 Measures of central tendency (median, mode).en.srt 19KB
08 Probability theory/078 What is probability_.en.srt 19KB
06 Descriptive statistics/054 Code_ Histogram bins.en.srt 19KB
16 Clustering and dimension-reduction/200 Independent components analysis (ICA).en.srt 18KB
04 What are (is_) data_/019 Sample vs. population data.en.srt 18KB
05 Visualizing data/022 Bar plots.en.srt 18KB
09 Hypothesis testing/113 Statistical significance vs. classification accuracy.en.srt 18KB
06 Descriptive statistics/038 Data distributions.en.srt 17KB
13 Analysis of Variance (ANOVA)/165 Two-way ANOVA example.en.srt 17KB
15 Statistical power and sample sizes/186 Estimating statistical power and sample size.en.srt 17KB
09 Hypothesis testing/112 Cross-validation.en.srt 17KB
10 The t-test family/125 Permutation testing for t-test significance.en.srt 17KB
07 Data normalizations and outliers/075 Code_ Data trimming to remove outliers.en.srt 17KB
08 Probability theory/085 Code_ compute probability mass functions.en.srt 17KB
05 Visualizing data/027 Histograms.en.srt 16KB
08 Probability theory/086 Cumulative probability distributions.en.srt 16KB
08 Probability theory/099 The Central Limit Theorem.en.srt 16KB
06 Descriptive statistics/058 Shannon entropy.en.srt 16KB
06 Descriptive statistics/056 Code_ violin plots.en.srt 16KB
12 Correlation/143 Partial correlation.en.srt 16KB
08 Probability theory/093 Expected value.en.srt 16KB
12 Correlation/151 Kendall's correlation for ordinal data.en.srt 16KB
08 Probability theory/080 Computing probabilities.en.srt 16KB
09 Hypothesis testing/104 Sample distributions under null and alternative hypotheses.en.srt 15KB
08 Probability theory/097 The Law of Large Numbers.en.srt 15KB
07 Data normalizations and outliers/072 Multivariate outlier detection.en.srt 15KB
14 Regression/168 Least-squares solution to the GLM.en.srt 15KB
06 Descriptive statistics/053 Histograms part 2_ Number of bins.en.srt 15KB
07 Data normalizations and outliers/062 Z-score standardization.en.srt 15KB
15 Statistical power and sample sizes/185 What is statistical power and why is it important_.en.srt 15KB
14 Regression/180 Code_ Logistic regression.en.srt 15KB
08 Probability theory/079 Probability vs. proportion.en.srt 15KB
07 Data normalizations and outliers/068 Removing outliers_ z-score method.en.srt 15KB
08 Probability theory/087 Code_ cdfs and pdfs.en.srt 15KB
12 Correlation/139 Correlation matrix.en.srt 14KB
14 Regression/171 Code_ simple regression.en.srt 14KB
14 Regression/176 Polynomial regression models.en.srt 14KB
01 Introductions/003 Statistics guessing game!.en.srt 14KB
02 Math prerequisites/012 The logistic function.en.srt 14KB
04 What are (is_) data_/018 Code_ representing types of data on computers.en.srt 14KB
11 Confidence intervals on parameters/128 What are confidence intervals and why do we need them_.en.srt 14KB
06 Descriptive statistics/052 Statistical _moments_.en.srt 14KB
08 Probability theory/091 Sampling variability, noise, and other annoyances.en.srt 14KB
09 Hypothesis testing/109 Parametric vs. non-parametric tests.en.srt 13KB
11 Confidence intervals on parameters/131 Confidence intervals via bootstrapping (resampling).en.srt 13KB
05 Visualizing data/025 Code_ box plots.en.srt 13KB
07 Data normalizations and outliers/073 Code_ Euclidean distance for outlier removal.en.srt 13KB
07 Data normalizations and outliers/065 Code_ min-max scaling.en.srt 13KB
09 Hypothesis testing/110 Multiple comparisons and Bonferroni correction.en.srt 13KB
05 Visualizing data/033 Linear vs. logarithmic axis scaling.en.srt 13KB
17 Signal detection theory/205 Response bias.en.srt 13KB
17 Signal detection theory/208 Code_ ROC curves.en.srt 12KB
10 The t-test family/115 One-sample t-test.en.srt 12KB
06 Descriptive statistics/037 Accuracy, precision, resolution.en.srt 12KB
12 Correlation/148 Code_ Spearman correlation and Fisher-Z.en.srt 12KB
17 Signal detection theory/207 Receiver operating characteristics (ROC).en.srt 11KB
05 Visualizing data/034 Code_ line plots.en.srt 11KB
12 Correlation/146 Nonparametric correlation_ Spearman rank.en.srt 11KB
13 Analysis of Variance (ANOVA)/159 The F-test and the ANOVA table.en.srt 11KB
10 The t-test family/121 Wilcoxon signed-rank (nonparametric t-test).en.srt 11KB
04 What are (is_) data_/021 The ethics of making up data.en.srt 11KB
06 Descriptive statistics/050 QQ plots.en.srt 11KB
09 Hypothesis testing/111 Statistical vs. theoretical vs. clinical significance.en.srt 10KB
08 Probability theory/096 Tree diagrams for conditional probabilities.en.srt 10KB
11 Confidence intervals on parameters/129 Computing confidence intervals via formula.en.srt 10KB
12 Correlation/145 The problem with Pearson.en.srt 10KB
12 Correlation/147 Fisher-Z transformation for correlations.en.srt 10KB
14 Regression/184 What to do about missing data.en.srt 10KB
02 Math prerequisites/013 Rank and tied-rank.en.srt 10KB
11 Confidence intervals on parameters/134 Misconceptions about confidence intervals.en.srt 9KB
09 Hypothesis testing/106 P-z combinations that you should memorize.en.srt 9KB
16 Clustering and dimension-reduction/195 K-nearest neighbor classification.en.srt 9KB
10 The t-test family/123 Mann-Whitney U test (nonparametric t-test).en.srt 9KB
02 Math prerequisites/008 Scientific notation.en.srt 9KB
17 Signal detection theory/202 The two perspectives of the world.en.srt 9KB
05 Visualizing data/032 When to use lines instead of bars.en.srt 9KB
07 Data normalizations and outliers/074 Removing outliers by data trimming.en.srt 9KB
05 Visualizing data/030 Pie charts.en.srt 9KB
04 What are (is_) data_/016 Where do data come from and what do they mean_.en.srt 9KB
01 Introductions/004 Using the Q&A forum.en.srt 8KB
02 Math prerequisites/011 Natural exponent and logarithm.en.srt 8KB
05 Visualizing data/024 Box-and-whisker plots.en.srt 8KB
10 The t-test family/124 Code_ Mann-Whitney U test.en.srt 8KB
10 The t-test family/127 _Unsupervised learning__ How many permutations_.en.srt 8KB
04 What are (is_) data_/020 Samples, case reports, and anecdotes.en.srt 8KB
06 Descriptive statistics/041 The beauty and simplicity of Normal.en.srt 8KB
12 Correlation/154 Cosine similarity.en.srt 8KB
07 Data normalizations and outliers/064 Min-max scaling.en.srt 8KB
06 Descriptive statistics/048 Interquartile range (IQR).en.srt 7KB
08 Probability theory/082 Probability and odds.en.srt 7KB
15 Statistical power and sample sizes/187 Compute power and sample size using G_Power.en.srt 7KB
10 The t-test family/120 _Unsupervised learning__ Importance of N for t-test.en.srt 7KB
06 Descriptive statistics/036 Descriptive vs. inferential statistics.en.srt 7KB
17 Signal detection theory/206 Code_ Response bias.en.srt 7KB
07 Data normalizations and outliers/076 Non-parametric solutions to outliers.en.srt 7KB
01 Introductions/001 [Important] Getting the most out of this course.en.srt 6KB
02 Math prerequisites/009 Summation notation.en.srt 6KB
01 Introductions/002 About using MATLAB or Python.en.srt 6KB
07 Data normalizations and outliers/069 The modified z-score method.en.srt 6KB
12 Correlation/142 _Unsupervised learning__ correlation to covariance matrix.en.srt 6KB
07 Data normalizations and outliers/061 Garbage in, garbage out (GIGO).en.srt 6KB
02 Math prerequisites/007 Arithmetic and exponents.en.srt 6KB
03 IMPORTANT_ Download course materials/014 Download materials for the entire course!.en.srt 6KB
06 Descriptive statistics/055 Violin plots.en.srt 5KB
16 Clustering and dimension-reduction/194 _Unsupervised learning__ dbscan vs. k-means.en.srt 5KB
06 Descriptive statistics/045 _Unsupervised learning__ central tendencies with outliers.en.srt 4KB
02 Math prerequisites/010 Absolute value.en.srt 4KB
02 Math prerequisites/006 Should you memorize statistical formulas_.en.srt 4KB
07 Data normalizations and outliers/077 An outlier lecture on personal accountability.en.srt 4KB
10 The t-test family/117 _Unsupervised learning__ The role of variance.en.srt 4KB
12 Correlation/141 _Unsupervised learning__ average correlation matrices.en.srt 4KB
18 Bonus section/211 Bonus content.html 4KB
06 Descriptive statistics/057 _Unsupervised learning__ asymmetric violin plots.en.srt 4KB
07 Data normalizations and outliers/071 _Unsupervised learning__ z vs. modified-z.en.srt 4KB
08 Probability theory/090 Monte Carlo sampling.en.srt 4KB
05 Visualizing data/026 _Unsupervised learning__ Boxplots of normal and uniform noise.en.srt 4KB
07 Data normalizations and outliers/066 _Unsupervised learning__ Invert the min-max scaling.en.srt 4KB
01 Introductions/003 stats-intro-GuessTheTest.zip 4KB
05 Visualizing data/029 _Unsupervised learning__ Histogram proportion.en.srt 4KB
12 Correlation/153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.en.srt 4KB
12 Correlation/150 _Unsupervised learning__ confidence interval on correlation.en.srt 3KB
08 Probability theory/088 _Unsupervised learning__ cdf's for various distributions.en.srt 3KB
08 Probability theory/101 _Unsupervised learning__ Averaging pairs of numbers.en.srt 3KB
08 Probability theory/083 _Unsupervised learning__ probabilities of odds-space.en.srt 3KB
01 Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.en.srt 3KB
06 Descriptive statistics/040 _Unsupervised learning__ histograms of distributions.en.srt 3KB
14 Regression/182 _Unsupervised learning__ Overfit data.en.srt 3KB
05 Visualizing data/035 _Unsupervised learning__ log-scaled plots.en.srt 3KB
16 Clustering and dimension-reduction/190 _Unsupervised learning__ K-means and normalization.en.srt 3KB
17 Signal detection theory/209 _Unsupervised learning__ Make this plot look nicer!.en.srt 2KB
04 What are (is_) data_/015 Is _data_ singular or plural_!_!!_!.en.srt 2KB
16 Clustering and dimension-reduction/199 _Unsupervised learning__ K-means on PC data.en.srt 2KB
06 Descriptive statistics/060 _Unsupervised learning__ entropy and number of bins.en.srt 2KB
16 Clustering and dimension-reduction/191 _Unsupervised learning__ K-means on a Gauss blur.en.srt 2KB
11 Confidence intervals on parameters/133 _Unsupervised learning__ Confidence intervals for variance.en.srt 2KB
12 Correlation/149 _Unsupervised learning__ Spearman correlation.en.srt 2KB
18 Bonus section/210 About deep learning.html 2KB
14 Regression/172 _Unsupervised learning__ Compute R2 and F.en.srt 1KB
14 Regression/178 _Unsupervised learning__ Polynomial design matrix.en.srt 1KB
0. Websites you may like/[FCS Forum].url 133B
0. Websites you may like/[FreeCourseSite.com].url 127B
0. Websites you may like/[CourseClub.ME].url 122B