Tutorialsplanet NET Udemy Master statistics machine learning intuition math code

Tutorialsplanet NET Udemy Master statistics machine learning intuition math code

Name	[Tutorialsplanet.NET] Udemy - Master statistics & machine learning - intuition, math, code	DOWNLOAD Copy Link
Total Size	13.8 GB
Total Files	674
Last Seen	2024-12-21 23:35
Hash	01E1CBFE829774CF8A044C9227D0CFBF4675C91B

/01 - Introductions/
001 [Important] Getting the most out of this course.mp4	40.1 MB
001 [Important] Getting the most out of this course_en.srt	6.2 KB
001 [Important] Getting the most out of this course_en.vtt	5.5 KB
002 About using MATLAB or Python.mp4	28.4 MB
002 About using MATLAB or Python_en.srt	6.1 KB
002 About using MATLAB or Python_en.vtt	5.3 KB
003 Statistics guessing game_.mp4	50.7 MB
003 Statistics guessing game__en.srt	13.6 KB
003 Statistics guessing game__en.vtt	11.8 KB
004 Using the Q&A forum.mp4	25.5 MB
004 Using the Q&A forum_en.srt	8.3 KB
004 Using the Q&A forum_en.vtt	7.2 KB
005 (optional) Entering time-stamped notes in the Udemy video player.mp4	7.4 MB
005 (optional) Entering time-stamped notes in the Udemy video player_en.srt	3.2 KB
005 (optional) Entering time-stamped notes in the Udemy video player_en.vtt	2.7 KB
25299297-stats-intro-GuessTheTest.zip	3.8 KB
[Tutorialsplanet.NET].url	0.1 KB
/02 - Math prerequisites/
001 Should you memorize statistical formulas_.mp4	29.4 MB
001 Should you memorize statistical formulas__en.srt	4.3 KB
001 Should you memorize statistical formulas__en.vtt	3.8 KB
002 Arithmetic and exponents.mp4	7.9 MB
002 Arithmetic and exponents_en.srt	5.8 KB
002 Arithmetic and exponents_en.vtt	5.1 KB
003 Scientific notation.mp4	13.5 MB
003 Scientific notation_en.srt	8.9 KB
003 Scientific notation_en.vtt	7.7 KB
004 Summation notation.mp4	8.1 MB
004 Summation notation_en.srt	6.1 KB
004 Summation notation_en.vtt	5.3 KB
005 Absolute value.mp4	7.3 MB
005 Absolute value_en.srt	4.3 KB
005 Absolute value_en.vtt	3.8 KB
006 Natural exponent and logarithm.mp4	12.8 MB
006 Natural exponent and logarithm_en.srt	8.2 KB
006 Natural exponent and logarithm_en.vtt	7.2 KB
007 The logistic function.mp4	18.8 MB
007 The logistic function_en.srt	13.4 KB
007 The logistic function_en.vtt	11.5 KB
008 Rank and tied-rank.mp4	13.6 MB
008 Rank and tied-rank_en.srt	9.8 KB
008 Rank and tied-rank_en.vtt	8.4 KB
/03 - IMPORTANT_ Download course materials/
001 Download materials for the entire course_.mp4	15.2 MB
001 Download materials for the entire course__en.srt	5.5 KB
001 Download materials for the entire course__en.vtt	4.9 KB
32684220-statsML.zip	1.4 MB
/04 - What are (is_) data_/
001 Is _data_ singular or plural_______.mp4	11.5 MB
001 Is _data_ singular or plural________en.srt	2.4 KB
001 Is _data_ singular or plural________en.vtt	2.1 KB
002 Where do data come from and what do they mean_.mp4	37.3 MB
002 Where do data come from and what do they mean__en.srt	8.6 KB
002 Where do data come from and what do they mean__en.vtt	7.5 KB
003 Types of data_ categorical, numerical, etc.mp4	62.2 MB
003 Types of data_ categorical, numerical, etc_en.srt	21.4 KB
003 Types of data_ categorical, numerical, etc_en.vtt	18.5 KB
004 Code_ representing types of data on computers.mp4	50.2 MB
004 Code_ representing types of data on computers_en.srt	13.4 KB
004 Code_ representing types of data on computers_en.vtt	11.4 KB
005 Sample vs. population data.mp4	38.9 MB
005 Sample vs. population data_en.srt	17.6 KB
005 Sample vs. population data_en.vtt	15.3 KB
006 Samples, case reports, and anecdotes.mp4	18.7 MB
006 Samples, case reports, and anecdotes_en.srt	7.9 KB
006 Samples, case reports, and anecdotes_en.vtt	6.9 KB
007 The ethics of making up data.mp4	20.6 MB
007 The ethics of making up data_en.srt	10.5 KB
007 The ethics of making up data_en.vtt	9.1 KB
/05 - Visualizing data/
001 Bar plots.mp4	38.6 MB
001 Bar plots_en.srt	17.5 KB
001 Bar plots_en.vtt	15.6 KB
002 Code_ bar plots.mp4	104.9 MB
002 Code_ bar plots_en.srt	26.0 KB
002 Code_ bar plots_en.vtt	23.2 KB
003 Box-and-whisker plots.mp4	11.7 MB
003 Box-and-whisker plots_en.srt	8.0 KB
003 Box-and-whisker plots_en.vtt	6.9 KB
004 Code_ box plots.mp4	87.7 MB
004 Code_ box plots_en.srt	13.1 KB
004 Code_ box plots_en.vtt	11.2 KB
005 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4	8.6 MB
005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.srt	3.8 KB
005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.vtt	3.3 KB
006 Histograms.mp4	45.9 MB
006 Histograms_en.srt	16.2 KB
006 Histograms_en.vtt	14.0 KB
007 Code_ histograms.mp4	140.0 MB
007 Code_ histograms_en.srt	24.8 KB
007 Code_ histograms_en.vtt	21.3 KB
008 _Unsupervised learning__ Histogram proportion.mp4	12.4 MB
008 _Unsupervised learning__ Histogram proportion_en.srt	3.5 KB
008 _Unsupervised learning__ Histogram proportion_en.vtt	3.0 KB
009 Pie charts.mp4	17.3 MB
009 Pie charts_en.srt	8.7 KB
009 Pie charts_en.vtt	7.5 KB
010 Code_ pie charts.mp4	82.8 MB
010 Code_ pie charts_en.srt	19.8 KB
010 Code_ pie charts_en.vtt	17.1 KB
011 When to use lines instead of bars.mp4	18.9 MB
011 When to use lines instead of bars_en.srt	8.8 KB
011 When to use lines instead of bars_en.vtt	7.6 KB
012 Linear vs. logarithmic axis scaling.mp4	26.9 MB
012 Linear vs. logarithmic axis scaling_en.srt	12.8 KB
012 Linear vs. logarithmic axis scaling_en.vtt	11.0 KB
013 Code_ line plots.mp4	39.1 MB
013 Code_ line plots_en.srt	11.1 KB
013 Code_ line plots_en.vtt	9.6 KB
014 _Unsupervised learning__ log-scaled plots.mp4	3.9 MB
014 _Unsupervised learning__ log-scaled plots_en.srt	2.5 KB
014 _Unsupervised learning__ log-scaled plots_en.vtt	2.2 KB
/06 - Descriptive statistics/
001 Descriptive vs. inferential statistics.mp4	22.5 MB
001 Descriptive vs. inferential statistics_en.srt	6.5 KB
001 Descriptive vs. inferential statistics_en.vtt	5.7 KB
002 Accuracy, precision, resolution.mp4	26.7 MB
002 Accuracy, precision, resolution_en.srt	11.7 KB
002 Accuracy, precision, resolution_en.vtt	10.0 KB
003 Data distributions.mp4	33.5 MB
003 Data distributions_en.srt	17.2 KB
003 Data distributions_en.vtt	14.9 KB
004 Code_ data from different distributions.mp4	317.8 MB
004 Code_ data from different distributions_en.srt	47.0 KB
004 Code_ data from different distributions_en.vtt	40.4 KB
005 _Unsupervised learning__ histograms of distributions.mp4	10.7 MB
005 _Unsupervised learning__ histograms of distributions_en.srt	3.1 KB
005 _Unsupervised learning__ histograms of distributions_en.vtt	2.7 KB
006 The beauty and simplicity of Normal.mp4	10.7 MB
006 The beauty and simplicity of Normal_en.srt	7.8 KB
006 The beauty and simplicity of Normal_en.vtt	6.9 KB
007 Measures of central tendency (mean).mp4	40.6 MB
007 Measures of central tendency (mean)_en.srt	19.4 KB
007 Measures of central tendency (mean)_en.vtt	16.7 KB
008 Measures of central tendency (median, mode).mp4	35.9 MB
008 Measures of central tendency (median, mode)_en.srt	18.6 KB
008 Measures of central tendency (median, mode)_en.vtt	16.1 KB
009 Code_ computing central tendency.mp4	69.8 MB
009 Code_ computing central tendency_en.srt	20.6 KB
009 Code_ computing central tendency_en.vtt	17.8 KB
010 _Unsupervised learning__ central tendencies with outliers.mp4	17.6 MB
010 _Unsupervised learning__ central tendencies with outliers_en.srt	4.4 KB
010 _Unsupervised learning__ central tendencies with outliers_en.vtt	3.9 KB
011 Measures of dispersion (variance, standard deviation).mp4	56.7 MB
011 Measures of dispersion (variance, standard deviation)_en.srt	26.9 KB
011 Measures of dispersion (variance, standard deviation)_en.vtt	23.1 KB
012 Code_ Computing dispersion.mp4	279.0 MB
012 Code_ Computing dispersion_en.srt	38.1 KB
012 Code_ Computing dispersion_en.vtt	33.1 KB
013 Interquartile range (IQR).mp4	10.3 MB
013 Interquartile range (IQR)_en.srt	7.2 KB
013 Interquartile range (IQR)_en.vtt	6.2 KB
014 Code_ IQR.mp4	87.4 MB
014 Code_ IQR_en.srt	24.0 KB
014 Code_ IQR_en.vtt	20.6 KB
015 QQ plots.mp4	17.0 MB
015 QQ plots_en.srt	10.4 KB
015 QQ plots_en.vtt	9.1 KB
016 Code_ QQ plots.mp4	94.7 MB
016 Code_ QQ plots_en.srt	24.0 KB
016 Code_ QQ plots_en.vtt	20.6 KB
017 Statistical _moments_.mp4	22.7 MB
017 Statistical _moments__en.srt	13.4 KB
017 Statistical _moments__en.vtt	11.4 KB
018 Histograms part 2_ Number of bins.mp4	24.6 MB
018 Histograms part 2_ Number of bins_en.srt	14.7 KB
018 Histograms part 2_ Number of bins_en.vtt	12.7 KB
019 Code_ Histogram bins.mp4	123.9 MB
019 Code_ Histogram bins_en.srt	18.3 KB
019 Code_ Histogram bins_en.vtt	15.8 KB
020 Violin plots.mp4	6.8 MB
020 Violin plots_en.srt	5.1 KB
020 Violin plots_en.vtt	4.4 KB
021 Code_ violin plots.mp4	110.1 MB
021 Code_ violin plots_en.srt	15.8 KB
021 Code_ violin plots_en.vtt	13.5 KB
022 _Unsupervised learning__ asymmetric violin plots.mp4	18.2 MB
022 _Unsupervised learning__ asymmetric violin plots_en.srt	3.9 KB
022 _Unsupervised learning__ asymmetric violin plots_en.vtt	3.4 KB
023 Shannon entropy.mp4	34.7 MB
023 Shannon entropy_en.srt	15.9 KB
023 Shannon entropy_en.vtt	13.8 KB
024 Code_ entropy.mp4	101.5 MB
024 Code_ entropy_en.srt	31.0 KB
024 Code_ entropy_en.vtt	26.5 KB
025 _Unsupervised learning__ entropy and number of bins.mp4	8.7 MB
025 _Unsupervised learning__ entropy and number of bins_en.srt	2.1 KB
025 _Unsupervised learning__ entropy and number of bins_en.vtt	1.8 KB
/07 - Data normalizations and outliers/
001 Garbage in, garbage out (GIGO).mp4	12.1 MB
001 Garbage in, garbage out (GIGO)_en.srt	5.8 KB
001 Garbage in, garbage out (GIGO)_en.vtt	5.1 KB
002 Z-score standardization.mp4	38.0 MB
002 Z-score standardization_en.srt	14.6 KB
002 Z-score standardization_en.vtt	12.6 KB
003 Code_ z-score.mp4	70.0 MB
003 Code_ z-score_en.srt	19.7 KB
003 Code_ z-score_en.vtt	17.0 KB
004 Min-max scaling.mp4	12.3 MB
004 Min-max scaling_en.srt	7.4 KB
004 Min-max scaling_en.vtt	6.4 KB
005 Code_ min-max scaling.mp4	42.4 MB
005 Code_ min-max scaling_en.srt	12.9 KB
005 Code_ min-max scaling_en.vtt	11.0 KB
006 _Unsupervised learning__ Invert the min-max scaling.mp4	7.1 MB
006 _Unsupervised learning__ Invert the min-max scaling_en.srt	3.7 KB
006 _Unsupervised learning__ Invert the min-max scaling_en.vtt	3.2 KB
007 What are outliers and why are they dangerous_.mp4	45.1 MB
007 What are outliers and why are they dangerous__en.srt	22.1 KB
007 What are outliers and why are they dangerous__en.vtt	19.0 KB
008 Removing outliers_ z-score method.mp4	35.1 MB
008 Removing outliers_ z-score method_en.srt	14.5 KB
008 Removing outliers_ z-score method_en.vtt	12.5 KB
009 The modified z-score method.mp4	10.1 MB
009 The modified z-score method_en.srt	6.0 KB
009 The modified z-score method_en.vtt	5.2 KB
010 Code_ z-score for outlier removal.mp4	143.5 MB
010 Code_ z-score for outlier removal_en.srt	34.5 KB
010 Code_ z-score for outlier removal_en.vtt	29.5 KB
011 _Unsupervised learning__ z vs. modified-z.mp4	9.5 MB
011 _Unsupervised learning__ z vs. modified-z_en.srt	3.9 KB
011 _Unsupervised learning__ z vs. modified-z_en.vtt	3.4 KB
012 Multivariate outlier detection.mp4	26.3 MB
012 Multivariate outlier detection_en.srt	14.7 KB
012 Multivariate outlier detection_en.vtt	12.6 KB
013 Code_ Euclidean distance for outlier removal.mp4	45.8 MB
013 Code_ Euclidean distance for outlier removal_en.srt	13.1 KB
013 Code_ Euclidean distance for outlier removal_en.vtt	11.3 KB
014 Removing outliers by data trimming.mp4	17.7 MB
014 Removing outliers by data trimming_en.srt	8.7 KB
014 Removing outliers by data trimming_en.vtt	7.6 KB
015 Code_ Data trimming to remove outliers.mp4	68.5 MB
015 Code_ Data trimming to remove outliers_en.srt	16.7 KB
015 Code_ Data trimming to remove outliers_en.vtt	14.4 KB
016 Non-parametric solutions to outliers.mp4	24.1 MB
016 Non-parametric solutions to outliers_en.srt	6.5 KB
016 Non-parametric solutions to outliers_en.vtt	5.7 KB
017 Nonlinear data transformations.mp4	35.3 MB
017 Nonlinear data transformations_en.srt	20.3 KB
017 Nonlinear data transformations_en.vtt	17.8 KB
018 An outlier lecture on personal accountability.mp4	18.6 MB
018 An outlier lecture on personal accountability_en.srt	4.2 KB
018 An outlier lecture on personal accountability_en.vtt	3.7 KB
/08 - Probability theory/
001 What is probability_.mp4	43.1 MB
001 What is probability__en.srt	18.4 KB
001 What is probability__en.vtt	15.9 KB
002 Probability vs. proportion.mp4	39.3 MB
002 Probability vs. proportion_en.srt	14.5 KB
002 Probability vs. proportion_en.vtt	12.4 KB
003 Computing probabilities.mp4	39.3 MB
003 Computing probabilities_en.srt	15.5 KB
003 Computing probabilities_en.vtt	13.4 KB
004 Code_ compute probabilities.mp4	155.6 MB
004 Code_ compute probabilities_en.srt	22.6 KB
004 Code_ compute probabilities_en.vtt	19.3 KB
005 Probability and odds.mp4	12.6 MB
005 Probability and odds_en.srt	7.1 KB
005 Probability and odds_en.vtt	6.2 KB
006 _Unsupervised learning__ probabilities of odds-space.mp4	6.2 MB
006 _Unsupervised learning__ probabilities of odds-space_en.srt	3.2 KB
006 _Unsupervised learning__ probabilities of odds-space_en.vtt	2.8 KB
007 Probability mass vs. density.mp4	140.7 MB
007 Probability mass vs. density_en.srt	18.9 KB
007 Probability mass vs. density_en.vtt	16.3 KB
008 Code_ compute probability mass functions.mp4	69.4 MB
008 Code_ compute probability mass functions_en.srt	16.4 KB
008 Code_ compute probability mass functions_en.vtt	14.4 KB
009 Cumulative distribution functions.mp4	47.6 MB
009 Cumulative distribution functions_en.srt	20.9 KB
009 Cumulative distribution functions_en.vtt	18.2 KB
010 Code_ cdfs and pdfs.mp4	100.6 MB
010 Code_ cdfs and pdfs_en.srt	14.8 KB
010 Code_ cdfs and pdfs_en.vtt	12.9 KB
011 _Unsupervised learning__ cdf's for various distributions.mp4	9.8 MB
011 _Unsupervised learning__ cdf's for various distributions_en.srt	3.4 KB
011 _Unsupervised learning__ cdf's for various distributions_en.vtt	3.0 KB
012 Creating sample estimate distributions.mp4	130.9 MB
012 Creating sample estimate distributions_en.srt	28.4 KB
012 Creating sample estimate distributions_en.vtt	24.4 KB
013 Monte Carlo sampling.mp4	9.3 MB
013 Monte Carlo sampling_en.srt	3.9 KB
013 Monte Carlo sampling_en.vtt	3.4 KB
014 Sampling variability, noise, and other annoyances.mp4	111.2 MB
014 Sampling variability, noise, and other annoyances_en.srt	13.4 KB
014 Sampling variability, noise, and other annoyances_en.vtt	11.6 KB
015 Code_ sampling variability.mp4	162.3 MB
015 Code_ sampling variability_en.srt	39.2 KB
015 Code_ sampling variability_en.vtt	33.7 KB
016 Expected value.mp4	62.5 MB
016 Expected value_en.srt	15.7 KB
016 Expected value_en.vtt	13.5 KB
017 Conditional probability.mp4	89.8 MB
017 Conditional probability_en.srt	19.3 KB
017 Conditional probability_en.vtt	16.5 KB
018 Code_ conditional probabilities.mp4	120.7 MB
018 Code_ conditional probabilities_en.srt	30.3 KB
018 Code_ conditional probabilities_en.vtt	26.0 KB
019 Tree diagrams for conditional probabilities.mp4	14.2 MB
019 Tree diagrams for conditional probabilities_en.srt	10.2 KB
019 Tree diagrams for conditional probabilities_en.vtt	8.8 KB
020 The Law of Large Numbers.mp4	42.5 MB
020 The Law of Large Numbers_en.srt	14.8 KB
020 The Law of Large Numbers_en.vtt	12.8 KB
021 Code_ Law of Large Numbers in action.mp4	173.6 MB
021 Code_ Law of Large Numbers in action_en.srt	28.5 KB
021 Code_ Law of Large Numbers in action_en.vtt	24.4 KB
022 The Central Limit Theorem.mp4	28.0 MB
022 The Central Limit Theorem_en.srt	15.9 KB
022 The Central Limit Theorem_en.vtt	13.8 KB
023 Code_ the CLT in action.mp4	97.9 MB
023 Code_ the CLT in action_en.srt	24.1 KB
023 Code_ the CLT in action_en.vtt	20.8 KB
024 _Unsupervised learning__ Averaging pairs of numbers.mp4	9.9 MB
024 _Unsupervised learning__ Averaging pairs of numbers_en.srt	3.3 KB
024 _Unsupervised learning__ Averaging pairs of numbers_en.vtt	2.8 KB
/09 - Hypothesis testing/
001 IVs, DVs, models, and other stats lingo.mp4	95.6 MB
001 IVs, DVs, models, and other stats lingo_en.srt	24.9 KB
001 IVs, DVs, models, and other stats lingo_en.vtt	21.4 KB
002 What is an hypothesis and how do you specify one_.mp4	51.5 MB
002 What is an hypothesis and how do you specify one__en.srt	23.8 KB
002 What is an hypothesis and how do you specify one__en.vtt	20.2 KB
003 Sample distributions under null and alternative hypotheses.mp4	45.9 MB
003 Sample distributions under null and alternative hypotheses_en.srt	15.0 KB
003 Sample distributions under null and alternative hypotheses_en.vtt	13.1 KB
004 P-values_ definition, tails, and misinterpretations.mp4	111.6 MB
004 P-values_ definition, tails, and misinterpretations_en.srt	26.0 KB
004 P-values_ definition, tails, and misinterpretations_en.vtt	22.8 KB
005 P-z combinations that you should memorize.mp4	18.2 MB
005 P-z combinations that you should memorize_en.srt	9.3 KB
005 P-z combinations that you should memorize_en.vtt	8.1 KB
006 Degrees of freedom.mp4	34.5 MB
006 Degrees of freedom_en.srt	2.7 KB
006 Degrees of freedom_en.vtt	16.4 KB
007 Type 1 and Type 2 errors.mp4	48.1 MB
007 Type 1 and Type 2 errors_en.srt	22.7 KB
007 Type 1 and Type 2 errors_en.vtt	19.5 KB
008 Parametric vs. non-parametric tests.mp4	91.7 MB
008 Parametric vs. non-parametric tests_en.srt	13.2 KB
008 Parametric vs. non-parametric tests_en.vtt	11.6 KB
009 Multiple comparisons and Bonferroni correction.mp4	31.0 MB
009 Multiple comparisons and Bonferroni correction_en.srt	12.8 KB
009 Multiple comparisons and Bonferroni correction_en.vtt	11.0 KB
010 Statistical vs. theoretical vs. clinical significance.mp4	20.0 MB
010 Statistical vs. theoretical vs. clinical significance_en.srt	10.2 KB
010 Statistical vs. theoretical vs. clinical significance_en.vtt	8.8 KB
011 Cross-validation.mp4	29.6 MB
011 Cross-validation_en.srt	16.8 KB
011 Cross-validation_en.vtt	14.7 KB
012 Statistical significance vs. classification accuracy.mp4	44.6 MB
012 Statistical significance vs. classification accuracy_en.srt	17.4 KB
012 Statistical significance vs. classification accuracy_en.vtt	15.0 KB
/10 - The t-test family/
001 Purpose and interpretation of the t-test.mp4	33.7 MB
001 Purpose and interpretation of the t-test_en.srt	19.4 KB
001 Purpose and interpretation of the t-test_en.vtt	16.8 KB
002 One-sample t-test.mp4	56.6 MB
002 One-sample t-test_en.srt	11.9 KB
002 One-sample t-test_en.vtt	10.3 KB
003 Code_ One-sample t-test.mp4	165.6 MB
003 Code_ One-sample t-test_en.srt	32.0 KB
003 Code_ One-sample t-test_en.vtt	27.3 KB
004 _Unsupervised learning__ The role of variance.mp4	30.0 MB
004 _Unsupervised learning__ The role of variance_en.srt	4.2 KB
004 _Unsupervised learning__ The role of variance_en.vtt	3.7 KB
005 Two-samples t-test.mp4	98.4 MB
005 Two-samples t-test_en.srt	19.4 KB
005 Two-samples t-test_en.vtt	16.8 KB
006 Code_ Two-samples t-test.mp4	221.6 MB
006 Code_ Two-samples t-test_en.srt	32.9 KB
006 Code_ Two-samples t-test_en.vtt	28.2 KB
007 _Unsupervised learning__ Importance of N for t-test.mp4	17.6 MB
007 _Unsupervised learning__ Importance of N for t-test_en.srt	7.0 KB
007 _Unsupervised learning__ Importance of N for t-test_en.vtt	6.1 KB
008 Wilcoxon signed-rank (nonparametric t-test).mp4	27.2 MB
008 Wilcoxon signed-rank (nonparametric t-test)_en.srt	10.7 KB
008 Wilcoxon signed-rank (nonparametric t-test)_en.vtt	9.3 KB
009 Code_ Signed-rank test.mp4	169.7 MB
009 Code_ Signed-rank test_en.srt	27.5 KB
009 Code_ Signed-rank test_en.vtt	23.6 KB
010 Mann-Whitney U test (nonparametric t-test).mp4	21.3 MB
010 Mann-Whitney U test (nonparametric t-test)_en.srt	9.1 KB
010 Mann-Whitney U test (nonparametric t-test)_en.vtt	7.8 KB
011 Code_ Mann-Whitney U test.mp4	54.6 MB
011 Code_ Mann-Whitney U test_en.srt	7.9 KB
011 Code_ Mann-Whitney U test_en.vtt	6.9 KB
012 Permutation testing for t-test significance.mp4	66.6 MB
012 Permutation testing for t-test significance_en.srt	16.7 KB
012 Permutation testing for t-test significance_en.vtt	14.5 KB
013 Code_ permutation testing.mp4	252.6 MB
013 Code_ permutation testing_en.srt	38.0 KB
013 Code_ permutation testing_en.vtt	32.5 KB
014 _Unsupervised learning__ How many permutations_.mp4	34.1 MB
014 _Unsupervised learning__ How many permutations__en.srt	7.9 KB
014 _Unsupervised learning__ How many permutations__en.vtt	6.9 KB
/11 - Confidence intervals on parameters/
001 What are confidence intervals and why do we need them_.mp4	31.3 MB
001 What are confidence intervals and why do we need them__en.srt	13.4 KB
001 What are confidence intervals and why do we need them__en.vtt	11.6 KB
002 Computing confidence intervals via formula.mp4	18.2 MB
002 Computing confidence intervals via formula_en.srt	9.7 KB
002 Computing confidence intervals via formula_en.vtt	8.4 KB
003 Code_ compute confidence intervals by formula.mp4	98.9 MB
003 Code_ compute confidence intervals by formula_en.srt	26.3 KB
003 Code_ compute confidence intervals by formula_en.vtt	22.6 KB
004 Confidence intervals via bootstrapping (resampling).mp4	56.9 MB
004 Confidence intervals via bootstrapping (resampling)_en.srt	13.1 KB
004 Confidence intervals via bootstrapping (resampling)_en.vtt	11.4 KB
005 Code_ bootstrapping confidence intervals.mp4	143.4 MB
005 Code_ bootstrapping confidence intervals_en.srt	22.2 KB
005 Code_ bootstrapping confidence intervals_en.vtt	18.9 KB
006 _Unsupervised learning__ Confidence intervals for variance.mp4	9.0 MB
006 _Unsupervised learning__ Confidence intervals for variance_en.srt	1.9 KB
006 _Unsupervised learning__ Confidence intervals for variance_en.vtt	1.7 KB
007 Misconceptions about confidence intervals.mp4	19.5 MB
007 Misconceptions about confidence intervals_en.srt	9.3 KB
007 Misconceptions about confidence intervals_en.vtt	8.1 KB
/12 - Correlation/
001 Motivation and description of correlation.mp4	124.2 MB
001 Motivation and description of correlation_en.srt	28.0 KB
001 Motivation and description of correlation_en.vtt	24.1 KB
002 Covariance and correlation_ formulas.mp4	43.9 MB
002 Covariance and correlation_ formulas_en.srt	21.3 KB
002 Covariance and correlation_ formulas_en.vtt	18.3 KB
003 Code_ correlation coefficient.mp4	224.5 MB
003 Code_ correlation coefficient_en.srt	41.4 KB
003 Code_ correlation coefficient_en.vtt	35.5 KB
004 Code_ Simulate data with specified correlation.mp4	73.5 MB
004 Code_ Simulate data with specified correlation_en.srt	20.5 KB
004 Code_ Simulate data with specified correlation_en.vtt	17.7 KB
005 Correlation matrix.mp4	32.5 MB
005 Correlation matrix_en.srt	13.9 KB
005 Correlation matrix_en.vtt	12.0 KB
006 Code_ correlation matrix.mp4	296.2 MB
006 Code_ correlation matrix_en.srt	32.6 KB
006 Code_ correlation matrix_en.vtt	27.8 KB
007 _Unsupervised learning__ average correlation matrices.mp4	19.4 MB
007 _Unsupervised learning__ average correlation matrices_en.srt	4.2 KB
007 _Unsupervised learning__ average correlation matrices_en.vtt	3.7 KB
008 _Unsupervised learning__ correlation to covariance matrix.mp4	10.6 MB
008 _Unsupervised learning__ correlation to covariance matrix_en.srt	6.0 KB
008 _Unsupervised learning__ correlation to covariance matrix_en.vtt	5.2 KB
009 Partial correlation.mp4	62.2 MB
009 Partial correlation_en.srt	15.8 KB
009 Partial correlation_en.vtt	13.7 KB
010 Code_ partial correlation.mp4	113.5 MB
010 Code_ partial correlation_en.srt	30.1 KB
010 Code_ partial correlation_en.vtt	25.8 KB
011 The problem with Pearson.mp4	17.4 MB
011 The problem with Pearson_en.srt	10.1 KB
011 The problem with Pearson_en.vtt	8.9 KB
012 Nonparametric correlation_ Spearman rank.mp4	24.9 MB
012 Nonparametric correlation_ Spearman rank_en.srt	11.0 KB
012 Nonparametric correlation_ Spearman rank_en.vtt	9.5 KB
013 Fisher-Z transformation for correlations.mp4	29.9 MB
013 Fisher-Z transformation for correlations_en.srt	10.1 KB
013 Fisher-Z transformation for correlations_en.vtt	8.8 KB
014 Code_ Spearman correlation and Fisher-Z.mp4	44.8 MB
014 Code_ Spearman correlation and Fisher-Z_en.srt	11.4 KB
014 Code_ Spearman correlation and Fisher-Z_en.vtt	9.8 KB
015 _Unsupervised learning__ Spearman correlation.mp4	16.7 MB
015 _Unsupervised learning__ Spearman correlation_en.srt	1.9 KB
015 _Unsupervised learning__ Spearman correlation_en.vtt	1.7 KB
016 _Unsupervised learning__ confidence interval on correlation.mp4	10.8 MB
016 _Unsupervised learning__ confidence interval on correlation_en.srt	3.4 KB
016 _Unsupervised learning__ confidence interval on correlation_en.vtt	3.0 KB
017 Kendall's correlation for ordinal data.mp4	31.6 MB
017 Kendall's correlation for ordinal data_en.srt	15.6 KB
017 Kendall's correlation for ordinal data_en.vtt	13.4 KB
018 Code_ Kendall correlation.mp4	193.2 MB
018 Code_ Kendall correlation_en.srt	18.0 KB
018 Code_ Kendall correlation_en.vtt	23.5 KB
019 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4	15.7 MB
019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.srt	3.4 KB
019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.vtt	3.0 KB
020 The subgroups correlation paradox.mp4	22.6 MB
020 The subgroups correlation paradox_en.srt	7.1 KB
020 The subgroups correlation paradox_en.vtt	6.3 KB
021 Cosine similarity.mp4	14.9 MB
021 Cosine similarity_en.srt	7.7 KB
021 Cosine similarity_en.vtt	6.7 KB
022 Code_ Cosine similarity vs. Pearson correlation.mp4	107.1 MB
022 Code_ Cosine similarity vs. Pearson correlation_en.srt	32.0 KB
022 Code_ Cosine similarity vs. Pearson correlation_en.vtt	27.6 KB
[Tutorialsplanet.NET].url	0.1 KB
/13 - Analysis of Variance (ANOVA)/
001 ANOVA intro, part1.mp4	144.4 MB
001 ANOVA intro, part1_en.srt	26.8 KB
001 ANOVA intro, part1_en.vtt	23.2 KB
002 ANOVA intro, part 2.mp4	88.3 MB
002 ANOVA intro, part 2_en.srt	29.1 KB
002 ANOVA intro, part 2_en.vtt	25.2 KB
003 Sum of squares.mp4	48.1 MB
003 Sum of squares_en.srt	26.2 KB
003 Sum of squares_en.vtt	22.9 KB
004 The F-test and the ANOVA table.mp4	20.9 MB
004 The F-test and the ANOVA table_en.srt	10.7 KB
004 The F-test and the ANOVA table_en.vtt	9.4 KB
005 The omnibus F-test and post-hoc comparisons.mp4	66.4 MB
005 The omnibus F-test and post-hoc comparisons_en.srt	19.3 KB
005 The omnibus F-test and post-hoc comparisons_en.vtt	16.6 KB
006 The two-way ANOVA.mp4	109.5 MB
006 The two-way ANOVA_en.srt	30.1 KB
006 The two-way ANOVA_en.vtt	25.8 KB
007 One-way ANOVA example.mp4	46.5 MB
007 One-way ANOVA example_en.srt	21.1 KB
007 One-way ANOVA example_en.vtt	18.1 KB
008 Code_ One-way ANOVA (independent samples).mp4	181.1 MB
008 Code_ One-way ANOVA (independent samples)_en.srt	26.3 KB
008 Code_ One-way ANOVA (independent samples)_en.vtt	22.5 KB
009 Code_ One-way repeated-measures ANOVA.mp4	76.7 MB
009 Code_ One-way repeated-measures ANOVA_en.srt	18.8 KB
009 Code_ One-way repeated-measures ANOVA_en.vtt	16.3 KB
010 Two-way ANOVA example.mp4	37.7 MB
010 Two-way ANOVA example_en.srt	16.5 KB
010 Two-way ANOVA example_en.vtt	14.3 KB
011 Code_ Two-way mixed ANOVA.mp4	119.7 MB
011 Code_ Two-way mixed ANOVA_en.srt	22.0 KB
011 Code_ Two-way mixed ANOVA_en.vtt	18.8 KB
/14 - Regression/
001 Introduction to GLM _ regression.mp4	65.0 MB
001 Introduction to GLM _ regression_en.srt	30.4 KB
001 Introduction to GLM _ regression_en.vtt	26.1 KB
002 Least-squares solution to the GLM.mp4	43.4 MB
002 Least-squares solution to the GLM_en.srt	14.7 KB
002 Least-squares solution to the GLM_en.vtt	12.6 KB
003 Evaluating regression models_ R2 and F.mp4	39.9 MB
003 Evaluating regression models_ R2 and F_en.srt	24.4 KB
003 Evaluating regression models_ R2 and F_en.vtt	21.0 KB
004 Simple regression.mp4	38.6 MB
004 Simple regression_en.srt	20.2 KB
004 Simple regression_en.vtt	17.4 KB
005 Code_ simple regression.mp4	54.8 MB
005 Code_ simple regression_en.srt	13.7 KB
005 Code_ simple regression_en.vtt	11.8 KB
006 _Unsupervised learning__ Compute R2 and F.mp4	5.6 MB
006 _Unsupervised learning__ Compute R2 and F_en.srt	1.5 KB
006 _Unsupervised learning__ Compute R2 and F_en.vtt	1.3 KB
007 Multiple regression.mp4	47.3 MB
007 Multiple regression_en.srt	19.6 KB
007 Multiple regression_en.vtt	16.9 KB
008 Standardizing regression coefficients.mp4	78.8 MB
008 Standardizing regression coefficients_en.srt	18.8 KB
008 Standardizing regression coefficients_en.vtt	16.1 KB
009 Code_ Multiple regression.mp4	179.3 MB
009 Code_ Multiple regression_en.srt	28.6 KB
009 Code_ Multiple regression_en.vtt	24.5 KB
010 Polynomial regression models.mp4	50.5 MB
010 Polynomial regression models_en.srt	12.5 KB
010 Polynomial regression models_en.vtt	10.9 KB
011 Code_ polynomial modeling.mp4	135.4 MB
011 Code_ polynomial modeling_en.srt	23.0 KB
011 Code_ polynomial modeling_en.vtt	19.8 KB
012 _Unsupervised learning__ Polynomial design matrix.mp4	5.0 MB
012 _Unsupervised learning__ Polynomial design matrix_en.srt	1.1 KB
012 _Unsupervised learning__ Polynomial design matrix_en.vtt	1.0 KB
013 Logistic regression.mp4	55.3 MB
013 Logistic regression_en.srt	26.1 KB
013 Logistic regression_en.vtt	22.3 KB
014 Code_ Logistic regression.mp4	85.2 MB
014 Code_ Logistic regression_en.srt	14.5 KB
014 Code_ Logistic regression_en.vtt	12.4 KB
015 Under- and over-fitting.mp4	126.7 MB
015 Under- and over-fitting_en.srt	26.0 KB
015 Under- and over-fitting_en.vtt	22.2 KB
016 _Unsupervised learning__ Overfit data.mp4	5.1 MB
016 _Unsupervised learning__ Overfit data_en.srt	2.8 KB
016 _Unsupervised learning__ Overfit data_en.vtt	2.4 KB
017 Comparing _nested_ models.mp4	41.0 MB
017 Comparing _nested_ models_en.srt	17.7 KB
017 Comparing _nested_ models_en.vtt	15.5 KB
018 What to do about missing data.mp4	16.8 MB
018 What to do about missing data_en.srt	9.8 KB
018 What to do about missing data_en.vtt	8.6 KB
/15 - Statistical power and sample sizes/
001 What is statistical power and why is it important_.mp4	41.4 MB
001 What is statistical power and why is it important__en.srt	14.7 KB
001 What is statistical power and why is it important__en.vtt	12.8 KB
002 Estimating statistical power and sample size.mp4	37.9 MB
002 Estimating statistical power and sample size_en.srt	17.0 KB
002 Estimating statistical power and sample size_en.vtt	14.7 KB
003 Compute power and sample size using G_Power.mp4	32.7 MB
003 Compute power and sample size using G_Power_en.srt	7.0 KB
003 Compute power and sample size using G_Power_en.vtt	5.9 KB
/16 - Clustering and dimension-reduction/
001 K-means clustering.mp4	56.9 MB
001 K-means clustering_en.srt	21.5 KB
001 K-means clustering_en.vtt	18.5 KB
002 Code_ k-means clustering.mp4	241.5 MB
002 Code_ k-means clustering_en.srt	35.2 KB
002 Code_ k-means clustering_en.vtt	30.1 KB
003 _Unsupervised learning__ K-means and normalization.mp4	13.5 MB
003 _Unsupervised learning__ K-means and normalization_en.srt	2.5 KB
003 _Unsupervised learning__ K-means and normalization_en.vtt	2.2 KB
004 _Unsupervised learning__ K-means on a Gauss blur.mp4	8.3 MB
004 _Unsupervised learning__ K-means on a Gauss blur_en.srt	2.0 KB
004 _Unsupervised learning__ K-means on a Gauss blur_en.vtt	1.8 KB
005 Clustering via dbscan.mp4	105.2 MB
005 Clustering via dbscan_en.srt	22.2 KB
005 Clustering via dbscan_en.vtt	19.1 KB
006 Code_ dbscan.mp4	302.1 MB
006 Code_ dbscan_en.srt	50.6 KB
006 Code_ dbscan_en.vtt	43.2 KB
007 _Unsupervised learning__ dbscan vs. k-means.mp4	20.9 MB
007 _Unsupervised learning__ dbscan vs. k-means_en.srt	4.5 KB
007 _Unsupervised learning__ dbscan vs. k-means_en.vtt	4.0 KB
008 K-nearest neighbor classification.mp4	13.1 MB
008 K-nearest neighbor classification_en.srt	9.2 KB
008 K-nearest neighbor classification_en.vtt	8.0 KB
009 Code_ KNN.mp4	113.6 MB
009 Code_ KNN_en.srt	18.6 KB
009 Code_ KNN_en.vtt	15.9 KB
010 Principal components analysis (PCA).mp4	44.6 MB
010 Principal components analysis (PCA)_en.srt	23.8 KB
010 Principal components analysis (PCA)_en.vtt	20.7 KB
011 Code_ PCA.mp4	183.6 MB
011 Code_ PCA_en.srt	27.2 KB
011 Code_ PCA_en.vtt	23.1 KB
012 _Unsupervised learning__ K-means on PC data.mp4	12.1 MB
012 _Unsupervised learning__ K-means on PC data_en.srt	2.3 KB
012 _Unsupervised learning__ K-means on PC data_en.vtt	2.0 KB
013 Independent components analysis (ICA).mp4	47.7 MB
013 Independent components analysis (ICA)_en.srt	17.7 KB
013 Independent components analysis (ICA)_en.vtt	15.4 KB
014 Code_ ICA.mp4	76.9 MB
014 Code_ ICA_en.srt	18.9 KB
014 Code_ ICA_en.vtt	16.3 KB
/17 - Signal detection theory/
001 The two perspectives of the world.mp4	14.6 MB
001 The two perspectives of the world_en.srt	8.9 KB
001 The two perspectives of the world_en.vtt	7.7 KB
002 d-prime.mp4	35.8 MB
002 d-prime_en.srt	19.7 KB
002 d-prime_en.vtt	16.8 KB
003 Code_ d-prime.mp4	72.9 MB
003 Code_ d-prime_en.srt	22.4 KB
003 Code_ d-prime_en.vtt	19.2 KB
004 Response bias.mp4	22.9 MB
004 Response bias_en.srt	12.5 KB
004 Response bias_en.vtt	10.8 KB
005 Code_ Response bias.mp4	23.9 MB
005 Code_ Response bias_en.srt	6.5 KB
005 Code_ Response bias_en.vtt	5.6 KB
006 F-score.mp4	112.5 MB
006 F-score_en.srt	33.9 KB
006 F-score_en.vtt	29.4 KB
007 Receiver operating characteristics (ROC).mp4	67.5 MB
007 Receiver operating characteristics (ROC)_en.srt	11.2 KB
007 Receiver operating characteristics (ROC)_en.vtt	9.8 KB
008 Code_ ROC curves.mp4	57.3 MB
008 Code_ ROC curves_en.srt	11.9 KB
008 Code_ ROC curves_en.vtt	10.4 KB
009 _Unsupervised learning__ Make this plot look nicer_.mp4	12.1 MB
009 _Unsupervised learning__ Make this plot look nicer__en.srt	2.4 KB
009 _Unsupervised learning__ Make this plot look nicer__en.vtt	2.1 KB
/18 - A real-world data journey/
001 Note about the code for this section.html	0.1 KB
002 Introduction.mp4	55.6 MB
002 Introduction_en.srt	6.4 KB
002 Introduction_en.vtt	5.6 KB
003 MATLAB_ Import and clean the marriage data.mp4	211.1 MB
003 MATLAB_ Import and clean the marriage data_en.srt	24.1 KB
003 MATLAB_ Import and clean the marriage data_en.vtt	21.0 KB
004 MATLAB_ Import the divorce data.mp4	101.0 MB
004 MATLAB_ Import the divorce data_en.srt	12.6 KB
004 MATLAB_ Import the divorce data_en.vtt	10.9 KB
005 MATLAB_ More data visualizations.mp4	36.0 MB
005 MATLAB_ More data visualizations_en.srt	9.5 KB
005 MATLAB_ More data visualizations_en.vtt	8.4 KB
006 MATLAB_ Inferential statistics.mp4	119.0 MB
006 MATLAB_ Inferential statistics_en.srt	15.7 KB
006 MATLAB_ Inferential statistics_en.vtt	13.7 KB
007 Python_ Import and clean the marriage data.mp4	262.0 MB
007 Python_ Import and clean the marriage data_en.srt	30.0 KB
007 Python_ Import and clean the marriage data_en.vtt	26.1 KB
008 Python_ Import the divorce data.mp4	143.8 MB
008 Python_ Import the divorce data_en.srt	19.0 KB
008 Python_ Import the divorce data_en.vtt	16.5 KB
009 Python_ Inferential statistics.mp4	121.2 MB
009 Python_ Inferential statistics_en.srt	16.5 KB
009 Python_ Inferential statistics_en.vtt	14.4 KB
010 Take-home messages.mp4	45.9 MB
010 Take-home messages_en.srt	8.9 KB
010 Take-home messages_en.vtt	7.8 KB
35855730-state-marriage-rates-90-95-99-19.xlsx	24.2 KB
35855734-state-divorce-rates-90-95-99-19.xlsx	23.0 KB
/19 - Bonus section/
001 About deep learning.html	0.6 KB
002 Bonus content.html	3.7 KB
[Tutorialsplanet.NET].url	0.1 KB
/
[Tutorialsplanet.NET].url	0.1 KB
Total files 674