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种子名称： [FreeCourseSite.com] Udemy - Master statistics and machine learning intuition math code
文件类型： 视频
文件数目： 209个文件
文件大小： 11.72 GB
收录时间： 2021-4-14 18:26
已经下载： 3次
资源热度： 224
最近下载： 2024-6-20 02:15

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

01 Introductions/001 [Important] Getting the most out of this course.mp438.04MB

01 Introductions/002 About using MATLAB or Python.mp438.91MB

01 Introductions/003 Statistics guessing game!.mp480.31MB

01 Introductions/004 Using the Q&A forum.mp424.47MB

01 Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.mp48.46MB

02 Math prerequisites/006 Should you memorize statistical formulas_.mp428.04MB

02 Math prerequisites/007 Arithmetic and exponents.mp47.62MB

02 Math prerequisites/008 Scientific notation.mp412.96MB

02 Math prerequisites/009 Summation notation.mp47.8MB

02 Math prerequisites/010 Absolute value.mp46.97MB

02 Math prerequisites/011 Natural exponent and logarithm.mp412.28MB

02 Math prerequisites/012 The logistic function.mp418.03MB

02 Math prerequisites/013 Rank and tied-rank.mp412.94MB

03 IMPORTANT_ Download course materials/014 Download materials for the entire course!.mp414.52MB

04 What are (is_) data_/015 Is _data_ singular or plural_!_!!_!.mp410.89MB

04 What are (is_) data_/016 Where do data come from and what do they mean_.mp435.62MB

04 What are (is_) data_/017 Types of data_ categorical, numerical, etc.mp459.62MB

04 What are (is_) data_/018 Code_ representing types of data on computers.mp447.94MB

04 What are (is_) data_/019 Sample vs. population data.mp437.27MB

04 What are (is_) data_/020 Samples, case reports, and anecdotes.mp417.88MB

04 What are (is_) data_/021 The ethics of making up data.mp419.76MB

05 Visualizing data/022 Bar plots.mp437.01MB

05 Visualizing data/023 Code_ bar plots.mp4100.24MB

05 Visualizing data/024 Box-and-whisker plots.mp411.21MB

05 Visualizing data/025 Code_ box plots.mp483.68MB

05 Visualizing data/026 _Unsupervised learning__ Boxplots of normal and uniform noise.mp48.27MB

05 Visualizing data/027 Histograms.mp443.91MB

05 Visualizing data/028 Code_ histograms.mp4133.75MB

05 Visualizing data/029 _Unsupervised learning__ Histogram proportion.mp411.83MB

05 Visualizing data/030 Pie charts.mp416.63MB

05 Visualizing data/031 Code_ pie charts.mp469.24MB

05 Visualizing data/032 When to use lines instead of bars.mp418.08MB

05 Visualizing data/033 Linear vs. logarithmic axis scaling.mp425.66MB

05 Visualizing data/034 Code_ line plots.mp437.42MB

05 Visualizing data/035 _Unsupervised learning__ log-scaled plots.mp43.75MB

06 Descriptive statistics/036 Descriptive vs. inferential statistics.mp421.56MB

06 Descriptive statistics/037 Accuracy, precision, resolution.mp425.54MB

06 Descriptive statistics/038 Data distributions.mp432.14MB

06 Descriptive statistics/039 Code_ data from different distributions.mp4303.53MB

06 Descriptive statistics/040 _Unsupervised learning__ histograms of distributions.mp410.21MB

06 Descriptive statistics/041 The beauty and simplicity of Normal.mp410.31MB

06 Descriptive statistics/042 Measures of central tendency (mean).mp438.91MB

06 Descriptive statistics/043 Measures of central tendency (median, mode).mp434.45MB

06 Descriptive statistics/044 Code_ computing central tendency.mp476.27MB

06 Descriptive statistics/045 _Unsupervised learning__ central tendencies with outliers.mp416.79MB

06 Descriptive statistics/046 Measures of dispersion (variance, standard deviation).mp454.41MB

06 Descriptive statistics/047 Code_ Computing dispersion.mp4266.53MB

06 Descriptive statistics/048 Interquartile range (IQR).mp49.91MB

06 Descriptive statistics/049 Code_ IQR.mp483.65MB

06 Descriptive statistics/050 QQ plots.mp416.34MB

06 Descriptive statistics/051 Code_ QQ plots.mp490.55MB

06 Descriptive statistics/052 Statistical _moments_.mp421.81MB

06 Descriptive statistics/053 Histograms part 2_ Number of bins.mp423.53MB

06 Descriptive statistics/054 Code_ Histogram bins.mp4118.27MB

06 Descriptive statistics/055 Violin plots.mp46.53MB

06 Descriptive statistics/056 Code_ violin plots.mp4105.08MB

06 Descriptive statistics/057 _Unsupervised learning__ asymmetric violin plots.mp417.37MB

06 Descriptive statistics/058 Shannon entropy.mp433.23MB

06 Descriptive statistics/059 Code_ entropy.mp4110.34MB

06 Descriptive statistics/060 _Unsupervised learning__ entropy and number of bins.mp48.27MB

07 Data normalizations and outliers/061 Garbage in, garbage out (GIGO).mp411.61MB

07 Data normalizations and outliers/062 Z-score standardization.mp436.38MB

07 Data normalizations and outliers/063 Code_ z-score.mp466.96MB

07 Data normalizations and outliers/064 Min-max scaling.mp411.74MB

07 Data normalizations and outliers/065 Code_ min-max scaling.mp440.53MB

07 Data normalizations and outliers/066 _Unsupervised learning__ Invert the min-max scaling.mp46.82MB

07 Data normalizations and outliers/067 What are outliers and why are they dangerous_.mp443.23MB

07 Data normalizations and outliers/068 Removing outliers_ z-score method.mp433.66MB

07 Data normalizations and outliers/069 The modified z-score method.mp49.68MB

07 Data normalizations and outliers/070 Code_ z-score for outlier removal.mp4137.22MB

07 Data normalizations and outliers/071 _Unsupervised learning__ z vs. modified-z.mp49.07MB

07 Data normalizations and outliers/072 Multivariate outlier detection.mp425.19MB

07 Data normalizations and outliers/073 Code_ Euclidean distance for outlier removal.mp443.84MB

07 Data normalizations and outliers/074 Removing outliers by data trimming.mp416.99MB

07 Data normalizations and outliers/075 Code_ Data trimming to remove outliers.mp465.43MB

07 Data normalizations and outliers/076 Non-parametric solutions to outliers.mp423.05MB

07 Data normalizations and outliers/077 An outlier lecture on personal accountability.mp417.83MB

08 Probability theory/078 What is probability_.mp441.32MB

08 Probability theory/079 Probability vs. proportion.mp437.66MB

08 Probability theory/080 Computing probabilities.mp437.69MB

08 Probability theory/081 Code_ compute probabilities.mp4137.11MB

08 Probability theory/082 Probability and odds.mp412.01MB

08 Probability theory/083 _Unsupervised learning__ probabilities of odds-space.mp45.96MB

08 Probability theory/084 Probability mass vs. density.mp4134.39MB

08 Probability theory/085 Code_ compute probability mass functions.mp466.29MB

08 Probability theory/086 Cumulative probability distributions.mp436.73MB

08 Probability theory/087 Code_ cdfs and pdfs.mp442.28MB

08 Probability theory/088 _Unsupervised learning__ cdf's for various distributions.mp49.35MB

08 Probability theory/089 Creating sample estimate distributions.mp4125.23MB

08 Probability theory/090 Monte Carlo sampling.mp416.35MB

08 Probability theory/091 Sampling variability, noise, and other annoyances.mp4106.24MB

08 Probability theory/092 Code_ sampling variability.mp4155.12MB

08 Probability theory/093 Expected value.mp459.79MB

08 Probability theory/094 Conditional probability.mp485.95MB

08 Probability theory/095 Code_ conditional probabilities.mp4115.37MB

08 Probability theory/096 Tree diagrams for conditional probabilities.mp413.61MB

08 Probability theory/097 The Law of Large Numbers.mp440.72MB

08 Probability theory/098 Code_ Law of Large Numbers in action.mp4165.91MB

08 Probability theory/099 The Central Limit Theorem.mp426.84MB

08 Probability theory/100 Code_ the CLT in action.mp493.57MB

08 Probability theory/101 _Unsupervised learning__ Averaging pairs of numbers.mp49.51MB

09 Hypothesis testing/102 IVs, DVs, models, and other stats lingo.mp491.48MB

09 Hypothesis testing/103 What is an hypothesis and how do you specify one_.mp449.37MB

09 Hypothesis testing/104 Sample distributions under null and alternative hypotheses.mp443.92MB

09 Hypothesis testing/105 P-values_ definition, tails, and misinterpretations.mp4131.88MB

09 Hypothesis testing/106 P-z combinations that you should memorize.mp417.33MB

09 Hypothesis testing/107 Degrees of freedom.mp433.1MB

09 Hypothesis testing/108 Type 1 and Type 2 errors.mp446.14MB

09 Hypothesis testing/109 Parametric vs. non-parametric tests.mp487.66MB

09 Hypothesis testing/110 Multiple comparisons and Bonferroni correction.mp429.7MB

09 Hypothesis testing/111 Statistical vs. theoretical vs. clinical significance.mp419.19MB

09 Hypothesis testing/112 Cross-validation.mp428.44MB

09 Hypothesis testing/113 Statistical significance vs. classification accuracy.mp442.69MB

10 The t-test family/114 Purpose and interpretation of the t-test.mp432.21MB

10 The t-test family/115 One-sample t-test.mp454.1MB

10 The t-test family/116 Code_ One-sample t-test.mp4158.23MB

10 The t-test family/117 _Unsupervised learning__ The role of variance.mp428.68MB

10 The t-test family/118 Two-samples t-test.mp493.81MB

10 The t-test family/119 Code_ Two-samples t-test.mp4211.61MB

10 The t-test family/120 _Unsupervised learning__ Importance of N for t-test.mp420.09MB

10 The t-test family/121 Wilcoxon signed-rank (nonparametric t-test).mp430.44MB

10 The t-test family/122 Code_ Signed-rank test.mp4162.12MB

10 The t-test family/123 Mann-Whitney U test (nonparametric t-test).mp420.41MB

10 The t-test family/124 Code_ Mann-Whitney U test.mp452.05MB

10 The t-test family/125 Permutation testing for t-test significance.mp463.66MB

10 The t-test family/126 Code_ permutation testing.mp4241.29MB

10 The t-test family/127 _Unsupervised learning__ How many permutations_.mp455.4MB

11 Confidence intervals on parameters/128 What are confidence intervals and why do we need them_.mp429.97MB

11 Confidence intervals on parameters/129 Computing confidence intervals via formula.mp417.44MB

11 Confidence intervals on parameters/130 Code_ compute confidence intervals by formula.mp4149.63MB

11 Confidence intervals on parameters/131 Confidence intervals via bootstrapping (resampling).mp454.41MB

11 Confidence intervals on parameters/132 Code_ bootstrapping confidence intervals.mp4136.76MB

11 Confidence intervals on parameters/133 _Unsupervised learning__ Confidence intervals for variance.mp48.57MB

11 Confidence intervals on parameters/134 Misconceptions about confidence intervals.mp418.7MB

12 Correlation/135 Motivation and description of correlation.mp496.65MB

12 Correlation/136 Covariance and correlation_ formulas.mp442.08MB

12 Correlation/137 Code_ correlation coefficient.mp4214.65MB

12 Correlation/138 Code_ Simulate data with specified correlation.mp4136.21MB

12 Correlation/139 Correlation matrix.mp431.12MB

12 Correlation/140 Code_ correlation matrix.mp4282.79MB

12 Correlation/141 _Unsupervised learning__ average correlation matrices.mp418.53MB

12 Correlation/142 _Unsupervised learning__ correlation to covariance matrix.mp410.2MB

12 Correlation/143 Partial correlation.mp459.54MB

12 Correlation/144 Code_ partial correlation.mp4108.26MB

12 Correlation/145 The problem with Pearson.mp416.69MB

12 Correlation/146 Nonparametric correlation_ Spearman rank.mp423.84MB

12 Correlation/147 Fisher-Z transformation for correlations.mp428.6MB

12 Correlation/148 Code_ Spearman correlation and Fisher-Z.mp442.81MB

12 Correlation/149 _Unsupervised learning__ Spearman correlation.mp415.96MB

12 Correlation/150 _Unsupervised learning__ confidence interval on correlation.mp48.9MB

12 Correlation/151 Kendall's correlation for ordinal data.mp430.32MB

12 Correlation/152 Code_ Kendall correlation.mp4184.47MB

12 Correlation/153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp414.95MB

12 Correlation/154 Cosine similarity.mp414.28MB

12 Correlation/155 Code_ Cosine similarity vs. Pearson correlation.mp4102.53MB

13 Analysis of Variance (ANOVA)/156 ANOVA intro, part1.mp4137.94MB

13 Analysis of Variance (ANOVA)/157 ANOVA intro, part 2.mp484.6MB

13 Analysis of Variance (ANOVA)/158 Sum of squares.mp446.02MB

13 Analysis of Variance (ANOVA)/159 The F-test and the ANOVA table.mp420.02MB

13 Analysis of Variance (ANOVA)/160 The omnibus F-test and post-hoc comparisons.mp463.61MB

13 Analysis of Variance (ANOVA)/161 The two-way ANOVA.mp4104.77MB

13 Analysis of Variance (ANOVA)/162 One-way ANOVA example.mp444.53MB

13 Analysis of Variance (ANOVA)/163 Code_ One-way ANOVA (independent samples).mp4172.94MB

13 Analysis of Variance (ANOVA)/164 Code_ One-way repeated-measures ANOVA.mp473.3MB

13 Analysis of Variance (ANOVA)/165 Two-way ANOVA example.mp435.83MB

13 Analysis of Variance (ANOVA)/166 Code_ Two-way mixed ANOVA.mp4114.36MB

14 Regression/167 Introduction to GLM _ regression.mp462.31MB

14 Regression/168 Least-squares solution to the GLM.mp441.59MB

14 Regression/169 Evaluating regression models_ R2 and F.mp438.33MB

14 Regression/170 Simple regression.mp436.98MB

14 Regression/171 Code_ simple regression.mp452.36MB

14 Regression/172 _Unsupervised learning__ Compute R2 and F.mp44.7MB

14 Regression/173 Multiple regression.mp469.08MB

14 Regression/174 Standardizing regression coefficients.mp447.47MB

14 Regression/175 Code_ Multiple regression.mp4171.33MB

14 Regression/176 Polynomial regression models.mp449.2MB

14 Regression/177 Code_ polynomial modeling.mp4129.33MB

14 Regression/178 _Unsupervised learning__ Polynomial design matrix.mp45.47MB

14 Regression/179 Logistic regression.mp452.98MB

14 Regression/180 Code_ Logistic regression.mp481.4MB

14 Regression/181 Under- and over-fitting.mp4121.15MB

14 Regression/182 _Unsupervised learning__ Overfit data.mp44.85MB

14 Regression/183 Comparing _nested_ models.mp439.3MB

14 Regression/184 What to do about missing data.mp416.15MB

15 Statistical power and sample sizes/185 What is statistical power and why is it important_.mp439.69MB

15 Statistical power and sample sizes/186 Estimating statistical power and sample size.mp431.07MB

15 Statistical power and sample sizes/187 Compute power and sample size using G_Power.mp431.24MB

16 Clustering and dimension-reduction/188 K-means clustering.mp454.51MB

16 Clustering and dimension-reduction/189 Code_ k-means clustering.mp4230.73MB

16 Clustering and dimension-reduction/190 _Unsupervised learning__ K-means and normalization.mp411.21MB

16 Clustering and dimension-reduction/191 _Unsupervised learning__ K-means on a Gauss blur.mp47.94MB

16 Clustering and dimension-reduction/192 Clustering via dbscan.mp4100.7MB

16 Clustering and dimension-reduction/193 Code_ dbscan.mp4288.67MB

16 Clustering and dimension-reduction/194 _Unsupervised learning__ dbscan vs. k-means.mp420MB

16 Clustering and dimension-reduction/195 K-nearest neighbor classification.mp412.57MB

16 Clustering and dimension-reduction/196 Code_ KNN.mp4108.6MB

16 Clustering and dimension-reduction/197 Principal components analysis (PCA).mp442.83MB

16 Clustering and dimension-reduction/198 Code_ PCA.mp473.1MB

16 Clustering and dimension-reduction/199 _Unsupervised learning__ K-means on PC data.mp411.6MB

16 Clustering and dimension-reduction/200 Independent components analysis (ICA).mp445.7MB

16 Clustering and dimension-reduction/201 Code_ ICA.mp473.53MB

17 Signal detection theory/202 The two perspectives of the world.mp414MB

17 Signal detection theory/203 d-prime.mp439.59MB

17 Signal detection theory/204 Code_ d-prime.mp469.75MB

17 Signal detection theory/205 Response bias.mp421.95MB

17 Signal detection theory/206 Code_ Response bias.mp422.9MB

17 Signal detection theory/207 Receiver operating characteristics (ROC).mp464.45MB

17 Signal detection theory/208 Code_ ROC curves.mp454.76MB

17 Signal detection theory/209 _Unsupervised learning__ Make this plot look nicer!.mp411.54MB