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[GigaCourse.Com] Udemy - Master statistics & machine learning - intuition, math, code
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0. Websites you may like
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[CourseClub.ME].url - 122 B
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[GigaCourse.Com].url - 49 B
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01 - Introductions
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001 [Important] Getting the most out of this course.mp4 - 38.26 MB
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001 [Important] Getting the most out of this course_en.srt - 6.08 KB
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001 [Important] Getting the most out of this course_en.vtt - 5.36 KB
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002 About using MATLAB or Python.mp4 - 27.11 MB
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002 About using MATLAB or Python_en.srt - 5.93 KB
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002 About using MATLAB or Python_en.vtt - 5.17 KB
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003 Statistics guessing game_.mp4 - 48.39 MB
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003 Statistics guessing game__en.srt - 13.33 KB
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003 Statistics guessing game__en.vtt - 11.52 KB
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004 Using the Q&A forum.mp4 - 24.36 MB
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004 Using the Q&A forum_en.srt - 8.13 KB
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004 Using the Q&A forum_en.vtt - 7.06 KB
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005 (optional) Entering time-stamped notes in the Udemy video player.mp4 - 7.06 MB
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005 (optional) Entering time-stamped notes in the Udemy video player_en.srt - 3.1 KB
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005 (optional) Entering time-stamped notes in the Udemy video player_en.vtt - 2.68 KB
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25299297-stats-intro-GuessTheTest.zip - 3.72 KB
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02 - Math prerequisites
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001 Should you memorize statistical formulas_.mp4 - 28 MB
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001 Should you memorize statistical formulas__en.srt - 4.17 KB
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001 Should you memorize statistical formulas__en.vtt - 3.69 KB
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002 Arithmetic and exponents.mp4 - 7.55 MB
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002 Arithmetic and exponents_en.srt - 5.63 KB
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002 Arithmetic and exponents_en.vtt - 4.94 KB
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003 Scientific notation.mp4 - 12.87 MB
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003 Scientific notation_en.srt - 8.72 KB
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003 Scientific notation_en.vtt - 7.48 KB
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004 Summation notation.mp4 - 7.73 MB
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004 Summation notation_en.srt - 6 KB
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004 Summation notation_en.vtt - 5.2 KB
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005 Absolute value.mp4 - 6.92 MB
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005 Absolute value_en.srt - 4.18 KB
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005 Absolute value_en.vtt - 3.68 KB
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006 Natural exponent and logarithm.mp4 - 12.18 MB
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006 Natural exponent and logarithm_en.srt - 8.05 KB
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006 Natural exponent and logarithm_en.vtt - 7 KB
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007 The logistic function.mp4 - 17.9 MB
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007 The logistic function_en.srt - 13.12 KB
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007 The logistic function_en.vtt - 11.28 KB
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008 Rank and tied-rank.mp4 - 12.92 MB
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008 Rank and tied-rank_en.srt - 9.55 KB
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008 Rank and tied-rank_en.vtt - 8.22 KB
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03 - IMPORTANT_ Download course materials
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001 Download materials for the entire course_.mp4 - 14.46 MB
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001 Download materials for the entire course__en.srt - 5.42 KB
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001 Download materials for the entire course__en.vtt - 4.77 KB
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32684220-statsML.zip - 1.36 MB
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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04 - What are (is_) data_
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001 Is _data_ singular or plural_______.mp4 - 10.92 MB
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001 Is _data_ singular or plural________en.srt - 2.33 KB
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001 Is _data_ singular or plural________en.vtt - 2.02 KB
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002 Where do data come from and what do they mean_.mp4 - 35.54 MB
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002 Where do data come from and what do they mean__en.srt - 8.4 KB
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002 Where do data come from and what do they mean__en.vtt - 7.29 KB
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003 Types of data_ categorical, numerical, etc.mp4 - 59.37 MB
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003 Types of data_ categorical, numerical, etc_en.srt - 20.93 KB
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003 Types of data_ categorical, numerical, etc_en.vtt - 18.09 KB
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004 Code_ representing types of data on computers.mp4 - 47.83 MB
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004 Code_ representing types of data on computers_en.srt - 13.09 KB
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004 Code_ representing types of data on computers_en.vtt - 11.15 KB
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005 Sample vs. population data.mp4 - 37.06 MB
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005 Sample vs. population data_en.srt - 17.21 KB
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005 Sample vs. population data_en.vtt - 14.93 KB
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006 Samples, case reports, and anecdotes.mp4 - 17.79 MB
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006 Samples, case reports, and anecdotes_en.srt - 7.68 KB
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006 Samples, case reports, and anecdotes_en.vtt - 6.73 KB
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007 The ethics of making up data.mp4 - 19.65 MB
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007 The ethics of making up data_en.srt - 10.29 KB
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007 The ethics of making up data_en.vtt - 8.9 KB
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05 - Visualizing data
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001 Bar plots.mp4 - 36.83 MB
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001 Bar plots_en.srt - 17.04 KB
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001 Bar plots_en.vtt - 15.28 KB
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002 Code_ bar plots.mp4 - 100.03 MB
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002 Code_ bar plots_en.srt - 25.39 KB
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002 Code_ bar plots_en.vtt - 22.66 KB
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003 Box-and-whisker plots.mp4 - 11.12 MB
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003 Box-and-whisker plots_en.srt - 7.82 KB
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003 Box-and-whisker plots_en.vtt - 6.79 KB
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004 Code_ box plots.mp4 - 83.65 MB
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004 Code_ box plots_en.srt - 12.75 KB
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004 Code_ box plots_en.vtt - 10.92 KB
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005 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4 - 8.24 MB
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005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.srt - 3.74 KB
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005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.vtt - 3.26 KB
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006 Histograms.mp4 - 43.73 MB
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006 Histograms_en.srt - 15.81 KB
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006 Histograms_en.vtt - 13.7 KB
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007 Code_ histograms.mp4 - 133.49 MB
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007 Code_ histograms_en.srt - 24.24 KB
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007 Code_ histograms_en.vtt - 20.8 KB
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008 _Unsupervised learning__ Histogram proportion.mp4 - 11.79 MB
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008 _Unsupervised learning__ Histogram proportion_en.srt - 3.4 KB
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008 _Unsupervised learning__ Histogram proportion_en.vtt - 2.98 KB
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009 Pie charts.mp4 - 16.53 MB
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009 Pie charts_en.srt - 8.47 KB
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009 Pie charts_en.vtt - 7.33 KB
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010 Code_ pie charts.mp4 - 78.92 MB
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010 Code_ pie charts_en.srt - 19.38 KB
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010 Code_ pie charts_en.vtt - 16.66 KB
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011 When to use lines instead of bars.mp4 - 17.98 MB
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011 When to use lines instead of bars_en.srt - 8.62 KB
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011 When to use lines instead of bars_en.vtt - 7.47 KB
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012 Linear vs. logarithmic axis scaling.mp4 - 25.64 MB
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012 Linear vs. logarithmic axis scaling_en.srt - 12.48 KB
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012 Linear vs. logarithmic axis scaling_en.vtt - 10.79 KB
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013 Code_ line plots.mp4 - 37.29 MB
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013 Code_ line plots_en.srt - 10.87 KB
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013 Code_ line plots_en.vtt - 9.41 KB
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014 _Unsupervised learning__ log-scaled plots.mp4 - 3.73 MB
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014 _Unsupervised learning__ log-scaled plots_en.srt - 2.47 KB
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014 _Unsupervised learning__ log-scaled plots_en.vtt - 2.14 KB
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06 - Descriptive statistics
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001 Descriptive vs. inferential statistics.mp4 - 21.48 MB
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001 Descriptive vs. inferential statistics_en.srt - 6.38 KB
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001 Descriptive vs. inferential statistics_en.vtt - 5.58 KB
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002 Accuracy, precision, resolution.mp4 - 25.42 MB
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002 Accuracy, precision, resolution_en.srt - 11.39 KB
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002 Accuracy, precision, resolution_en.vtt - 9.81 KB
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003 Data distributions.mp4 - 31.95 MB
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003 Data distributions_en.srt - 16.76 KB
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003 Data distributions_en.vtt - 14.52 KB
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004 Code_ data from different distributions.mp4 - 303.11 MB
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004 Code_ data from different distributions_en.srt - 45.95 KB
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004 Code_ data from different distributions_en.vtt - 39.47 KB
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005 _Unsupervised learning__ histograms of distributions.mp4 - 10.18 MB
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005 _Unsupervised learning__ histograms of distributions_en.srt - 3.06 KB
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005 _Unsupervised learning__ histograms of distributions_en.vtt - 2.63 KB
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006 The beauty and simplicity of Normal.mp4 - 10.23 MB
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006 The beauty and simplicity of Normal_en.srt - 7.64 KB
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006 The beauty and simplicity of Normal_en.vtt - 6.72 KB
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007 Measures of central tendency (mean).mp4 - 38.7 MB
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007 Measures of central tendency (mean)_en.srt - 18.99 KB
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007 Measures of central tendency (mean)_en.vtt - 16.34 KB
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008 Measures of central tendency (median, mode).mp4 - 34.26 MB
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008 Measures of central tendency (median, mode)_en.srt - 18.21 KB
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008 Measures of central tendency (median, mode)_en.vtt - 15.71 KB
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009 Code_ computing central tendency.mp4 - 66.6 MB
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009 Code_ computing central tendency_en.srt - 20.14 KB
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009 Code_ computing central tendency_en.vtt - 17.41 KB
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010 _Unsupervised learning__ central tendencies with outliers.mp4 - 16.74 MB
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010 _Unsupervised learning__ central tendencies with outliers_en.srt - 4.31 KB
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010 _Unsupervised learning__ central tendencies with outliers_en.vtt - 3.78 KB
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011 Measures of dispersion (variance, standard deviation).mp4 - 54.12 MB
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011 Measures of dispersion (variance, standard deviation)_en.srt - 26.26 KB
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011 Measures of dispersion (variance, standard deviation)_en.vtt - 22.56 KB
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012 Code_ Computing dispersion.mp4 - 266.09 MB
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012 Code_ Computing dispersion_en.srt - 37.23 KB
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012 Code_ Computing dispersion_en.vtt - 32.31 KB
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013 Interquartile range (IQR).mp4 - 9.84 MB
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013 Interquartile range (IQR)_en.srt - 7.01 KB
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013 Interquartile range (IQR)_en.vtt - 6.1 KB
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014 Code_ IQR.mp4 - 83.39 MB
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014 Code_ IQR_en.srt - 23.44 KB
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014 Code_ IQR_en.vtt - 20.15 KB
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015 QQ plots.mp4 - 16.22 MB
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015 QQ plots_en.srt - 10.18 KB
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015 QQ plots_en.vtt - 8.88 KB
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016 Code_ QQ plots.mp4 - 90.3 MB
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016 Code_ QQ plots_en.srt - 23.48 KB
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016 Code_ QQ plots_en.vtt - 20.1 KB
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017 Statistical _moments_.mp4 - 21.68 MB
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017 Statistical _moments__en.srt - 13.06 KB
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017 Statistical _moments__en.vtt - 11.16 KB
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018 Histograms part 2_ Number of bins.mp4 - 23.5 MB
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018 Histograms part 2_ Number of bins_en.srt - 14.32 KB
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018 Histograms part 2_ Number of bins_en.vtt - 12.41 KB
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019 Code_ Histogram bins.mp4 - 118.12 MB
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019 Code_ Histogram bins_en.srt - 17.86 KB
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019 Code_ Histogram bins_en.vtt - 15.4 KB
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020 Violin plots.mp4 - 6.47 MB
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020 Violin plots_en.srt - 4.98 KB
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020 Violin plots_en.vtt - 4.3 KB
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021 Code_ violin plots.mp4 - 104.96 MB
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021 Code_ violin plots_en.srt - 15.42 KB
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021 Code_ violin plots_en.vtt - 13.19 KB
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022 _Unsupervised learning__ asymmetric violin plots.mp4 - 17.32 MB
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022 _Unsupervised learning__ asymmetric violin plots_en.srt - 3.84 KB
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022 _Unsupervised learning__ asymmetric violin plots_en.vtt - 3.31 KB
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023 Shannon entropy.mp4 - 33.05 MB
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023 Shannon entropy_en.srt - 15.53 KB
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023 Shannon entropy_en.vtt - 13.46 KB
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024 Code_ entropy.mp4 - 96.76 MB
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024 Code_ entropy_en.srt - 30.29 KB
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024 Code_ entropy_en.vtt - 25.84 KB
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025 _Unsupervised learning__ entropy and number of bins.mp4 - 8.25 MB
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025 _Unsupervised learning__ entropy and number of bins_en.srt - 2.01 KB
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025 _Unsupervised learning__ entropy and number of bins_en.vtt - 1.76 KB
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07 - Data normalizations and outliers
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001 Garbage in, garbage out (GIGO).mp4 - 11.55 MB
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001 Garbage in, garbage out (GIGO)_en.srt - 5.69 KB
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001 Garbage in, garbage out (GIGO)_en.vtt - 5 KB
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002 Z-score standardization.mp4 - 36.23 MB
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002 Z-score standardization_en.srt - 14.29 KB
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002 Z-score standardization_en.vtt - 12.3 KB
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003 Code_ z-score.mp4 - 66.77 MB
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003 Code_ z-score_en.srt - 19.26 KB
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003 Code_ z-score_en.vtt - 16.61 KB
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004 Min-max scaling.mp4 - 11.73 MB
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004 Min-max scaling_en.srt - 7.23 KB
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004 Min-max scaling_en.vtt - 6.3 KB
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005 Code_ min-max scaling.mp4 - 40.43 MB
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005 Code_ min-max scaling_en.srt - 12.56 KB
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005 Code_ min-max scaling_en.vtt - 10.75 KB
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006 _Unsupervised learning__ Invert the min-max scaling.mp4 - 6.79 MB
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006 _Unsupervised learning__ Invert the min-max scaling_en.srt - 3.63 KB
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006 _Unsupervised learning__ Invert the min-max scaling_en.vtt - 3.17 KB
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007 What are outliers and why are they dangerous_.mp4 - 43 MB
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007 What are outliers and why are they dangerous__en.srt - 21.54 KB
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007 What are outliers and why are they dangerous__en.vtt - 18.52 KB
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008 Removing outliers_ z-score method.mp4 - 33.51 MB
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008 Removing outliers_ z-score method_en.srt - 14.14 KB
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008 Removing outliers_ z-score method_en.vtt - 12.21 KB
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009 The modified z-score method.mp4 - 9.62 MB
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009 The modified z-score method_en.srt - 5.9 KB
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009 The modified z-score method_en.vtt - 5.12 KB
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010 Code_ z-score for outlier removal.mp4 - 136.89 MB
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010 Code_ z-score for outlier removal_en.srt - 33.64 KB
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010 Code_ z-score for outlier removal_en.vtt - 28.78 KB
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011 _Unsupervised learning__ z vs. modified-z.mp4 - 9.02 MB
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011 _Unsupervised learning__ z vs. modified-z_en.srt - 3.84 KB
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011 _Unsupervised learning__ z vs. modified-z_en.vtt - 3.36 KB
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012 Multivariate outlier detection.mp4 - 25.05 MB
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012 Multivariate outlier detection_en.srt - 14.35 KB
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012 Multivariate outlier detection_en.vtt - 12.28 KB
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013 Code_ Euclidean distance for outlier removal.mp4 - 43.72 MB
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013 Code_ Euclidean distance for outlier removal_en.srt - 12.77 KB
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013 Code_ Euclidean distance for outlier removal_en.vtt - 11.04 KB
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014 Removing outliers by data trimming.mp4 - 16.9 MB
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014 Removing outliers by data trimming_en.srt - 8.53 KB
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014 Removing outliers by data trimming_en.vtt - 7.4 KB
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015 Code_ Data trimming to remove outliers.mp4 - 65.29 MB
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015 Code_ Data trimming to remove outliers_en.srt - 16.3 KB
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015 Code_ Data trimming to remove outliers_en.vtt - 14.05 KB
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016 Non-parametric solutions to outliers.mp4 - 22.96 MB
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016 Non-parametric solutions to outliers_en.srt - 6.34 KB
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016 Non-parametric solutions to outliers_en.vtt - 5.57 KB
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017 Nonlinear data transformations.mp4 - 33.69 MB
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017 Nonlinear data transformations_en.srt - 19.8 KB
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017 Nonlinear data transformations_en.vtt - 17.38 KB
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018 An outlier lecture on personal accountability.mp4 - 17.7 MB
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018 An outlier lecture on personal accountability_en.srt - 4.12 KB
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018 An outlier lecture on personal accountability_en.vtt - 3.65 KB
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08 - Probability theory
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001 What is probability_.mp4 - 41.11 MB
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001 What is probability__en.srt - 17.94 KB
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001 What is probability__en.vtt - 15.49 KB
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002 Probability vs. proportion.mp4 - 37.52 MB
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002 Probability vs. proportion_en.srt - 14.14 KB
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002 Probability vs. proportion_en.vtt - 12.13 KB
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003 Computing probabilities.mp4 - 37.52 MB
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003 Computing probabilities_en.srt - 15.15 KB
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003 Computing probabilities_en.vtt - 13.08 KB
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004 Code_ compute probabilities.mp4 - 148.4 MB
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004 Code_ compute probabilities_en.srt - 22.04 KB
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004 Code_ compute probabilities_en.vtt - 18.88 KB
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005 Probability and odds.mp4 - 12.01 MB
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005 Probability and odds_en.srt - 6.94 KB
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005 Probability and odds_en.vtt - 6.01 KB
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006 _Unsupervised learning__ probabilities of odds-space.mp4 - 5.92 MB
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006 _Unsupervised learning__ probabilities of odds-space_en.srt - 3.14 KB
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006 _Unsupervised learning__ probabilities of odds-space_en.vtt - 2.76 KB
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007 Probability mass vs. density.mp4 - 134.14 MB
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007 Probability mass vs. density_en.srt - 18.42 KB
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007 Probability mass vs. density_en.vtt - 15.95 KB
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008 Code_ compute probability mass functions.mp4 - 66.17 MB
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008 Code_ compute probability mass functions_en.srt - 16.01 KB
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008 Code_ compute probability mass functions_en.vtt - 14.03 KB
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009 Cumulative distribution functions.mp4 - 45.41 MB
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009 Cumulative distribution functions_en.srt - 20.37 KB
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009 Cumulative distribution functions_en.vtt - 17.73 KB
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010 Code_ cdfs and pdfs.mp4 - 95.94 MB
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010 Code_ cdfs and pdfs_en.srt - 14.49 KB
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010 Code_ cdfs and pdfs_en.vtt - 12.56 KB
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011 _Unsupervised learning__ cdf's for various distributions.mp4 - 9.31 MB
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011 _Unsupervised learning__ cdf's for various distributions_en.srt - 3.32 KB
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011 _Unsupervised learning__ cdf's for various distributions_en.vtt - 2.94 KB
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012 Creating sample estimate distributions.mp4 - 124.85 MB
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012 Creating sample estimate distributions_en.srt - 27.73 KB
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012 Creating sample estimate distributions_en.vtt - 23.83 KB
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013 Monte Carlo sampling.mp4 - 8.83 MB
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013 Monte Carlo sampling_en.srt - 3.82 KB
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013 Monte Carlo sampling_en.vtt - 3.35 KB
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014 Sampling variability, noise, and other annoyances.mp4 - 106.08 MB
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014 Sampling variability, noise, and other annoyances_en.srt - 13.06 KB
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014 Sampling variability, noise, and other annoyances_en.vtt - 11.37 KB
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015 Code_ sampling variability.mp4 - 154.75 MB
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015 Code_ sampling variability_en.srt - 38.25 KB
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015 Code_ sampling variability_en.vtt - 32.9 KB
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016 Expected value.mp4 - 59.63 MB
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016 Expected value_en.srt - 15.37 KB
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016 Expected value_en.vtt - 13.21 KB
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017 Conditional probability.mp4 - 85.68 MB
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017 Conditional probability_en.srt - 18.81 KB
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017 Conditional probability_en.vtt - 16.12 KB
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018 Code_ conditional probabilities.mp4 - 115.08 MB
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018 Code_ conditional probabilities_en.srt - 29.61 KB
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018 Code_ conditional probabilities_en.vtt - 25.37 KB
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019 Tree diagrams for conditional probabilities.mp4 - 13.5 MB
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019 Tree diagrams for conditional probabilities_en.srt - 9.92 KB
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019 Tree diagrams for conditional probabilities_en.vtt - 8.57 KB
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020 The Law of Large Numbers.mp4 - 40.55 MB
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020 The Law of Large Numbers_en.srt - 14.41 KB
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020 The Law of Large Numbers_en.vtt - 12.47 KB
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021 Code_ Law of Large Numbers in action.mp4 - 165.6 MB
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021 Code_ Law of Large Numbers in action_en.srt - 27.83 KB
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021 Code_ Law of Large Numbers in action_en.vtt - 23.84 KB
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022 The Central Limit Theorem.mp4 - 26.67 MB
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022 The Central Limit Theorem_en.srt - 15.56 KB
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022 The Central Limit Theorem_en.vtt - 13.52 KB
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023 Code_ the CLT in action.mp4 - 93.32 MB
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023 Code_ the CLT in action_en.srt - 23.58 KB
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023 Code_ the CLT in action_en.vtt - 20.3 KB
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024 _Unsupervised learning__ Averaging pairs of numbers.mp4 - 9.48 MB
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024 _Unsupervised learning__ Averaging pairs of numbers_en.srt - 3.19 KB
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024 _Unsupervised learning__ Averaging pairs of numbers_en.vtt - 2.76 KB
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09 - Hypothesis testing
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001 IVs, DVs, models, and other stats lingo.mp4 - 91.14 MB
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001 IVs, DVs, models, and other stats lingo_en.srt - 24.3 KB
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001 IVs, DVs, models, and other stats lingo_en.vtt - 20.93 KB
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002 What is an hypothesis and how do you specify one_.mp4 - 49.12 MB
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002 What is an hypothesis and how do you specify one__en.srt - 23.28 KB
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002 What is an hypothesis and how do you specify one__en.vtt - 19.71 KB
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003 Sample distributions under null and alternative hypotheses.mp4 - 43.75 MB
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003 Sample distributions under null and alternative hypotheses_en.srt - 14.68 KB
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003 Sample distributions under null and alternative hypotheses_en.vtt - 12.79 KB
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004 P-values_ definition, tails, and misinterpretations.mp4 - 106.47 MB
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004 P-values_ definition, tails, and misinterpretations_en.srt - 25.43 KB
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004 P-values_ definition, tails, and misinterpretations_en.vtt - 22.25 KB
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005 P-z combinations that you should memorize.mp4 - 17.32 MB
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005 P-z combinations that you should memorize_en.srt - 9.04 KB
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005 P-z combinations that you should memorize_en.vtt - 7.89 KB
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006 Degrees of freedom.mp4 - 32.9 MB
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006 Degrees of freedom_en.srt - 2.6 KB
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006 Degrees of freedom_en.vtt - 16 KB
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007 Type 1 and Type 2 errors.mp4 - 45.9 MB
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007 Type 1 and Type 2 errors_en.srt - 22.2 KB
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007 Type 1 and Type 2 errors_en.vtt - 19.01 KB
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008 Parametric vs. non-parametric tests.mp4 - 87.45 MB
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008 Parametric vs. non-parametric tests_en.srt - 12.87 KB
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008 Parametric vs. non-parametric tests_en.vtt - 11.3 KB
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009 Multiple comparisons and Bonferroni correction.mp4 - 29.56 MB
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009 Multiple comparisons and Bonferroni correction_en.srt - 12.5 KB
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009 Multiple comparisons and Bonferroni correction_en.vtt - 10.79 KB
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010 Statistical vs. theoretical vs. clinical significance.mp4 - 19.08 MB
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010 Statistical vs. theoretical vs. clinical significance_en.srt - 9.98 KB
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010 Statistical vs. theoretical vs. clinical significance_en.vtt - 8.64 KB
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011 Cross-validation.mp4 - 28.25 MB
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011 Cross-validation_en.srt - 16.44 KB
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011 Cross-validation_en.vtt - 14.32 KB
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012 Statistical significance vs. classification accuracy.mp4 - 42.5 MB
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012 Statistical significance vs. classification accuracy_en.srt - 17.02 KB
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012 Statistical significance vs. classification accuracy_en.vtt - 14.68 KB
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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10 - The t-test family
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001 Purpose and interpretation of the t-test.mp4 - 32.16 MB
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001 Purpose and interpretation of the t-test_en.srt - 18.92 KB
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001 Purpose and interpretation of the t-test_en.vtt - 16.41 KB
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002 One-sample t-test.mp4 - 53.95 MB
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002 One-sample t-test_en.srt - 11.59 KB
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002 One-sample t-test_en.vtt - 10.04 KB
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003 Code_ One-sample t-test.mp4 - 157.96 MB
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003 Code_ One-sample t-test_en.srt - 31.24 KB
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003 Code_ One-sample t-test_en.vtt - 26.62 KB
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004 _Unsupervised learning__ The role of variance.mp4 - 28.65 MB
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004 _Unsupervised learning__ The role of variance_en.srt - 4.11 KB
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004 _Unsupervised learning__ The role of variance_en.vtt - 3.58 KB
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005 Two-samples t-test.mp4 - 93.81 MB
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005 Two-samples t-test_en.srt - 18.97 KB
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005 Two-samples t-test_en.vtt - 16.4 KB
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006 Code_ Two-samples t-test.mp4 - 211.35 MB
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006 Code_ Two-samples t-test_en.srt - 32.17 KB
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006 Code_ Two-samples t-test_en.vtt - 27.56 KB
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007 _Unsupervised learning__ Importance of N for t-test.mp4 - 16.77 MB
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007 _Unsupervised learning__ Importance of N for t-test_en.srt - 6.85 KB
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007 _Unsupervised learning__ Importance of N for t-test_en.vtt - 5.94 KB
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008 Wilcoxon signed-rank (nonparametric t-test).mp4 - 25.98 MB
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008 Wilcoxon signed-rank (nonparametric t-test)_en.srt - 10.43 KB
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008 Wilcoxon signed-rank (nonparametric t-test)_en.vtt - 9.09 KB
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009 Code_ Signed-rank test.mp4 - 161.85 MB
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009 Code_ Signed-rank test_en.srt - 26.9 KB
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009 Code_ Signed-rank test_en.vtt - 23.02 KB
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010 Mann-Whitney U test (nonparametric t-test).mp4 - 20.32 MB
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010 Mann-Whitney U test (nonparametric t-test)_en.srt - 8.84 KB
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010 Mann-Whitney U test (nonparametric t-test)_en.vtt - 7.66 KB
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011 Code_ Mann-Whitney U test.mp4 - 52.05 MB
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011 Code_ Mann-Whitney U test_en.srt - 7.75 KB
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011 Code_ Mann-Whitney U test_en.vtt - 6.7 KB
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012 Permutation testing for t-test significance.mp4 - 63.48 MB
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012 Permutation testing for t-test significance_en.srt - 16.36 KB
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012 Permutation testing for t-test significance_en.vtt - 14.2 KB
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013 Code_ permutation testing.mp4 - 240.9 MB
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013 Code_ permutation testing_en.srt - 37.1 KB
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013 Code_ permutation testing_en.vtt - 31.77 KB
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014 _Unsupervised learning__ How many permutations_.mp4 - 32.5 MB
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014 _Unsupervised learning__ How many permutations__en.srt - 7.74 KB
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014 _Unsupervised learning__ How many permutations__en.vtt - 6.73 KB
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11 - Confidence intervals on parameters
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001 What are confidence intervals and why do we need them_.mp4 - 29.83 MB
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001 What are confidence intervals and why do we need them__en.srt - 13.12 KB
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001 What are confidence intervals and why do we need them__en.vtt - 11.33 KB
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002 Computing confidence intervals via formula.mp4 - 17.33 MB
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002 Computing confidence intervals via formula_en.srt - 9.42 KB
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002 Computing confidence intervals via formula_en.vtt - 8.22 KB
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003 Code_ compute confidence intervals by formula.mp4 - 94.29 MB
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003 Code_ compute confidence intervals by formula_en.srt - 25.68 KB
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003 Code_ compute confidence intervals by formula_en.vtt - 22.06 KB
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004 Confidence intervals via bootstrapping (resampling).mp4 - 54.27 MB
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004 Confidence intervals via bootstrapping (resampling)_en.srt - 12.83 KB
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004 Confidence intervals via bootstrapping (resampling)_en.vtt - 11.18 KB
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005 Code_ bootstrapping confidence intervals.mp4 - 136.71 MB
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005 Code_ bootstrapping confidence intervals_en.srt - 21.66 KB
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005 Code_ bootstrapping confidence intervals_en.vtt - 18.41 KB
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006 _Unsupervised learning__ Confidence intervals for variance.mp4 - 8.54 MB
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006 _Unsupervised learning__ Confidence intervals for variance_en.srt - 1.89 KB
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006 _Unsupervised learning__ Confidence intervals for variance_en.vtt - 1.68 KB
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007 Misconceptions about confidence intervals.mp4 - 18.6 MB
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007 Misconceptions about confidence intervals_en.srt - 9.08 KB
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007 Misconceptions about confidence intervals_en.vtt - 7.91 KB
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12 - Correlation
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001 Motivation and description of correlation.mp4 - 118.43 MB
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001 Motivation and description of correlation_en.srt - 27.37 KB
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001 Motivation and description of correlation_en.vtt - 23.58 KB
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002 Covariance and correlation_ formulas.mp4 - 41.85 MB
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002 Covariance and correlation_ formulas_en.srt - 20.8 KB
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002 Covariance and correlation_ formulas_en.vtt - 17.86 KB
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003 Code_ correlation coefficient.mp4 - 214.14 MB
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003 Code_ correlation coefficient_en.srt - 40.44 KB
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003 Code_ correlation coefficient_en.vtt - 34.65 KB
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004 Code_ Simulate data with specified correlation.mp4 - 70.12 MB
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004 Code_ Simulate data with specified correlation_en.srt - 20.03 KB
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004 Code_ Simulate data with specified correlation_en.vtt - 17.33 KB
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005 Correlation matrix.mp4 - 30.96 MB
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005 Correlation matrix_en.srt - 13.6 KB
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005 Correlation matrix_en.vtt - 11.72 KB
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006 Code_ correlation matrix.mp4 - 282.48 MB
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006 Code_ correlation matrix_en.srt - 31.87 KB
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006 Code_ correlation matrix_en.vtt - 27.17 KB
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007 _Unsupervised learning__ average correlation matrices.mp4 - 18.49 MB
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007 _Unsupervised learning__ average correlation matrices_en.srt - 4.07 KB
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007 _Unsupervised learning__ average correlation matrices_en.vtt - 3.57 KB
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008 _Unsupervised learning__ correlation to covariance matrix.mp4 - 10.13 MB
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008 _Unsupervised learning__ correlation to covariance matrix_en.srt - 5.81 KB
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008 _Unsupervised learning__ correlation to covariance matrix_en.vtt - 5.12 KB
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009 Partial correlation.mp4 - 59.34 MB
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009 Partial correlation_en.srt - 15.42 KB
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009 Partial correlation_en.vtt - 13.36 KB
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010 Code_ partial correlation.mp4 - 108.27 MB
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010 Code_ partial correlation_en.srt - 29.41 KB
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010 Code_ partial correlation_en.vtt - 25.23 KB
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011 The problem with Pearson.mp4 - 16.57 MB
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011 The problem with Pearson_en.srt - 9.89 KB
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011 The problem with Pearson_en.vtt - 8.65 KB
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012 Nonparametric correlation_ Spearman rank.mp4 - 23.72 MB
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012 Nonparametric correlation_ Spearman rank_en.srt - 10.73 KB
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012 Nonparametric correlation_ Spearman rank_en.vtt - 9.3 KB
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013 Fisher-Z transformation for correlations.mp4 - 28.48 MB
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013 Fisher-Z transformation for correlations_en.srt - 9.87 KB
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013 Fisher-Z transformation for correlations_en.vtt - 8.63 KB
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014 Code_ Spearman correlation and Fisher-Z.mp4 - 42.71 MB
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014 Code_ Spearman correlation and Fisher-Z_en.srt - 11.1 KB
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014 Code_ Spearman correlation and Fisher-Z_en.vtt - 9.61 KB
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015 _Unsupervised learning__ Spearman correlation.mp4 - 15.95 MB
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015 _Unsupervised learning__ Spearman correlation_en.srt - 1.86 KB
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015 _Unsupervised learning__ Spearman correlation_en.vtt - 1.64 KB
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016 _Unsupervised learning__ confidence interval on correlation.mp4 - 10.31 MB
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016 _Unsupervised learning__ confidence interval on correlation_en.srt - 3.32 KB
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016 _Unsupervised learning__ confidence interval on correlation_en.vtt - 2.93 KB
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017 Kendall's correlation for ordinal data.mp4 - 30.15 MB
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017 Kendall's correlation for ordinal data_en.srt - 15.22 KB
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017 Kendall's correlation for ordinal data_en.vtt - 13.13 KB
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018 Code_ Kendall correlation.mp4 - 184.22 MB
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018 Code_ Kendall correlation_en.srt - 17.59 KB
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018 Code_ Kendall correlation_en.vtt - 22.96 KB
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019 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 - 14.95 MB
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019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.srt - 3.31 KB
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019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.vtt - 2.95 KB
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020 The subgroups correlation paradox.mp4 - 21.57 MB
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020 The subgroups correlation paradox_en.srt - 6.98 KB
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020 The subgroups correlation paradox_en.vtt - 6.13 KB
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021 Cosine similarity.mp4 - 14.2 MB
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021 Cosine similarity_en.srt - 7.49 KB
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021 Cosine similarity_en.vtt - 6.54 KB
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022 Code_ Cosine similarity vs. Pearson correlation.mp4 - 102.19 MB
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022 Code_ Cosine similarity vs. Pearson correlation_en.srt - 31.27 KB
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022 Code_ Cosine similarity vs. Pearson correlation_en.vtt - 26.93 KB
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13 - Analysis of Variance (ANOVA)
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001 ANOVA intro, part1.mp4 - 137.72 MB
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001 ANOVA intro, part1_en.srt - 26.2 KB
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001 ANOVA intro, part1_en.vtt - 22.63 KB
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002 ANOVA intro, part 2.mp4 - 84.25 MB
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002 ANOVA intro, part 2_en.srt - 28.45 KB
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002 ANOVA intro, part 2_en.vtt - 24.57 KB
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003 Sum of squares.mp4 - 45.88 MB
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003 Sum of squares_en.srt - 25.6 KB
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003 Sum of squares_en.vtt - 22.32 KB
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004 The F-test and the ANOVA table.mp4 - 19.9 MB
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004 The F-test and the ANOVA table_en.srt - 10.47 KB
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004 The F-test and the ANOVA table_en.vtt - 9.18 KB
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005 The omnibus F-test and post-hoc comparisons.mp4 - 63.36 MB
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005 The omnibus F-test and post-hoc comparisons_en.srt - 18.85 KB
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005 The omnibus F-test and post-hoc comparisons_en.vtt - 16.24 KB
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006 The two-way ANOVA.mp4 - 104.39 MB
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006 The two-way ANOVA_en.srt - 29.37 KB
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006 The two-way ANOVA_en.vtt - 25.18 KB
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007 One-way ANOVA example.mp4 - 44.32 MB
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007 One-way ANOVA example_en.srt - 20.6 KB
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007 One-way ANOVA example_en.vtt - 17.68 KB
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008 Code_ One-way ANOVA (independent samples).mp4 - 172.72 MB
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008 Code_ One-way ANOVA (independent samples)_en.srt - 25.72 KB
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008 Code_ One-way ANOVA (independent samples)_en.vtt - 21.93 KB
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009 Code_ One-way repeated-measures ANOVA.mp4 - 73.1 MB
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009 Code_ One-way repeated-measures ANOVA_en.srt - 18.38 KB
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009 Code_ One-way repeated-measures ANOVA_en.vtt - 15.87 KB
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010 Two-way ANOVA example.mp4 - 35.95 MB
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010 Two-way ANOVA example_en.srt - 16.08 KB
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010 Two-way ANOVA example_en.vtt - 14 KB
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011 Code_ Two-way mixed ANOVA.mp4 - 114.16 MB
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011 Code_ Two-way mixed ANOVA_en.srt - 21.44 KB
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011 Code_ Two-way mixed ANOVA_en.vtt - 18.37 KB
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14 - Regression
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001 Introduction to GLM _ regression.mp4 - 61.97 MB
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001 Introduction to GLM _ regression_en.srt - 29.73 KB
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001 Introduction to GLM _ regression_en.vtt - 25.51 KB
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002 Least-squares solution to the GLM.mp4 - 41.41 MB
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002 Least-squares solution to the GLM_en.srt - 14.33 KB
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002 Least-squares solution to the GLM_en.vtt - 12.35 KB
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003 Evaluating regression models_ R2 and F.mp4 - 38.06 MB
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003 Evaluating regression models_ R2 and F_en.srt - 23.82 KB
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003 Evaluating regression models_ R2 and F_en.vtt - 20.49 KB
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004 Simple regression.mp4 - 36.77 MB
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004 Simple regression_en.srt - 19.71 KB
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004 Simple regression_en.vtt - 16.98 KB
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005 Code_ simple regression.mp4 - 52.29 MB
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005 Code_ simple regression_en.srt - 13.43 KB
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005 Code_ simple regression_en.vtt - 11.54 KB
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006 _Unsupervised learning__ Compute R2 and F.mp4 - 5.38 MB
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006 _Unsupervised learning__ Compute R2 and F_en.srt - 1.44 KB
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006 _Unsupervised learning__ Compute R2 and F_en.vtt - 1.28 KB
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007 Multiple regression.mp4 - 45.14 MB
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007 Multiple regression_en.srt - 19.12 KB
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007 Multiple regression_en.vtt - 16.47 KB
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008 Standardizing regression coefficients.mp4 - 75.19 MB
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008 Standardizing regression coefficients_en.srt - 18.34 KB
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008 Standardizing regression coefficients_en.vtt - 15.73 KB
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009 Code_ Multiple regression.mp4 - 170.95 MB
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009 Code_ Multiple regression_en.srt - 27.91 KB
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009 Code_ Multiple regression_en.vtt - 23.9 KB
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010 Polynomial regression models.mp4 - 48.15 MB
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010 Polynomial regression models_en.srt - 12.22 KB
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010 Polynomial regression models_en.vtt - 10.65 KB
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011 Code_ polynomial modeling.mp4 - 129.08 MB
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011 Code_ polynomial modeling_en.srt - 22.44 KB
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011 Code_ polynomial modeling_en.vtt - 19.3 KB
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012 _Unsupervised learning__ Polynomial design matrix.mp4 - 4.74 MB
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012 _Unsupervised learning__ Polynomial design matrix_en.srt - 1.11 KB
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012 _Unsupervised learning__ Polynomial design matrix_en.vtt - 1010 B
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013 Logistic regression.mp4 - 52.7 MB
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013 Logistic regression_en.srt - 25.46 KB
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013 Logistic regression_en.vtt - 21.81 KB
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014 Code_ Logistic regression.mp4 - 81.23 MB
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014 Code_ Logistic regression_en.srt - 14.18 KB
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014 Code_ Logistic regression_en.vtt - 12.14 KB
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015 Under- and over-fitting.mp4 - 120.86 MB
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015 Under- and over-fitting_en.srt - 25.37 KB
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015 Under- and over-fitting_en.vtt - 21.71 KB
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016 _Unsupervised learning__ Overfit data.mp4 - 4.82 MB
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016 _Unsupervised learning__ Overfit data_en.srt - 2.69 KB
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016 _Unsupervised learning__ Overfit data_en.vtt - 2.36 KB
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017 Comparing _nested_ models.mp4 - 39.07 MB
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017 Comparing _nested_ models_en.srt - 17.28 KB
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017 Comparing _nested_ models_en.vtt - 15.11 KB
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018 What to do about missing data.mp4 - 16.05 MB
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018 What to do about missing data_en.srt - 9.59 KB
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018 What to do about missing data_en.vtt - 8.36 KB
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15 - Statistical power and sample sizes
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001 What is statistical power and why is it important_.mp4 - 39.53 MB
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001 What is statistical power and why is it important__en.srt - 14.33 KB
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001 What is statistical power and why is it important__en.vtt - 12.5 KB
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002 Estimating statistical power and sample size.mp4 - 36.16 MB
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002 Estimating statistical power and sample size_en.srt - 16.57 KB
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002 Estimating statistical power and sample size_en.vtt - 14.35 KB
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003 Compute power and sample size using G_Power.mp4 - 31.2 MB
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003 Compute power and sample size using G_Power_en.srt - 6.82 KB
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003 Compute power and sample size using G_Power_en.vtt - 5.8 KB
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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16 - Clustering and dimension-reduction
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001 K-means clustering.mp4 - 54.29 MB
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001 K-means clustering_en.srt - 21.01 KB
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001 K-means clustering_en.vtt - 18.08 KB
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002 Code_ k-means clustering.mp4 - 230.34 MB
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002 Code_ k-means clustering_en.srt - 34.34 KB
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002 Code_ k-means clustering_en.vtt - 29.39 KB
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003 _Unsupervised learning__ K-means and normalization.mp4 - 12.91 MB
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003 _Unsupervised learning__ K-means and normalization_en.srt - 2.48 KB
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003 _Unsupervised learning__ K-means and normalization_en.vtt - 2.19 KB
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004 _Unsupervised learning__ K-means on a Gauss blur.mp4 - 7.94 MB
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004 _Unsupervised learning__ K-means on a Gauss blur_en.srt - 2 KB
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004 _Unsupervised learning__ K-means on a Gauss blur_en.vtt - 1.75 KB
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005 Clustering via dbscan.mp4 - 100.3 MB
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005 Clustering via dbscan_en.srt - 21.67 KB
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005 Clustering via dbscan_en.vtt - 18.65 KB
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006 Code_ dbscan.mp4 - 288.12 MB
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006 Code_ dbscan_en.srt - 49.38 KB
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006 Code_ dbscan_en.vtt - 42.2 KB
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007 _Unsupervised learning__ dbscan vs. k-means.mp4 - 19.94 MB
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007 _Unsupervised learning__ dbscan vs. k-means_en.srt - 4.43 KB
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007 _Unsupervised learning__ dbscan vs. k-means_en.vtt - 3.88 KB
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008 K-nearest neighbor classification.mp4 - 12.47 MB
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008 K-nearest neighbor classification_en.srt - 8.99 KB
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008 K-nearest neighbor classification_en.vtt - 7.84 KB
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009 Code_ KNN.mp4 - 108.34 MB
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009 Code_ KNN_en.srt - 18.21 KB
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009 Code_ KNN_en.vtt - 15.49 KB
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010 Principal components analysis (PCA).mp4 - 42.56 MB
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010 Principal components analysis (PCA)_en.srt - 23.28 KB
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010 Principal components analysis (PCA)_en.vtt - 20.26 KB
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011 Code_ PCA.mp4 - 175.1 MB
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011 Code_ PCA_en.srt - 26.52 KB
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011 Code_ PCA_en.vtt - 22.6 KB
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012 _Unsupervised learning__ K-means on PC data.mp4 - 11.52 MB
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012 _Unsupervised learning__ K-means on PC data_en.srt - 2.21 KB
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012 _Unsupervised learning__ K-means on PC data_en.vtt - 1.95 KB
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013 Independent components analysis (ICA).mp4 - 45.52 MB
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013 Independent components analysis (ICA)_en.srt - 17.25 KB
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013 Independent components analysis (ICA)_en.vtt - 15.07 KB
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014 Code_ ICA.mp4 - 73.36 MB
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014 Code_ ICA_en.srt - 18.44 KB
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014 Code_ ICA_en.vtt - 15.9 KB
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17 - Signal detection theory
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001 The two perspectives of the world.mp4 - 13.91 MB
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001 The two perspectives of the world_en.srt - 8.71 KB
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001 The two perspectives of the world_en.vtt - 7.52 KB
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002 d-prime.mp4 - 34.14 MB
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002 d-prime_en.srt - 19.21 KB
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002 d-prime_en.vtt - 16.45 KB
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003 Code_ d-prime.mp4 - 69.5 MB
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003 Code_ d-prime_en.srt - 21.85 KB
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003 Code_ d-prime_en.vtt - 18.77 KB
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004 Response bias.mp4 - 21.82 MB
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004 Response bias_en.srt - 12.25 KB
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004 Response bias_en.vtt - 10.56 KB
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005 Code_ Response bias.mp4 - 22.81 MB
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005 Code_ Response bias_en.srt - 6.35 KB
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005 Code_ Response bias_en.vtt - 5.49 KB
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006 F-score.mp4 - 107.25 MB
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006 F-score_en.srt - 33.08 KB
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006 F-score_en.vtt - 28.75 KB
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007 Receiver operating characteristics (ROC).mp4 - 64.37 MB
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007 Receiver operating characteristics (ROC)_en.srt - 10.95 KB
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007 Receiver operating characteristics (ROC)_en.vtt - 9.58 KB
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008 Code_ ROC curves.mp4 - 54.62 MB
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008 Code_ ROC curves_en.srt - 11.67 KB
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008 Code_ ROC curves_en.vtt - 10.14 KB
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009 _Unsupervised learning__ Make this plot look nicer_.mp4 - 11.5 MB
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009 _Unsupervised learning__ Make this plot look nicer__en.srt - 2.35 KB
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009 _Unsupervised learning__ Make this plot look nicer__en.vtt - 2.06 KB
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18 - A real-world data journey
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001 Note about the code for this section.html - 135 B
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002 Introduction.mp4 - 53.02 MB
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002 Introduction_en.srt - 6.23 KB
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002 Introduction_en.vtt - 5.44 KB
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003 MATLAB_ Import and clean the marriage data.mp4 - 201.29 MB
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003 MATLAB_ Import and clean the marriage data_en.srt - 23.57 KB
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003 MATLAB_ Import and clean the marriage data_en.vtt - 20.52 KB
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004 MATLAB_ Import the divorce data.mp4 - 96.29 MB
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004 MATLAB_ Import the divorce data_en.srt - 12.31 KB
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004 MATLAB_ Import the divorce data_en.vtt - 10.65 KB
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005 MATLAB_ More data visualizations.mp4 - 34.32 MB
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005 MATLAB_ More data visualizations_en.srt - 9.26 KB
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005 MATLAB_ More data visualizations_en.vtt - 8.16 KB
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006 MATLAB_ Inferential statistics.mp4 - 113.52 MB
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006 MATLAB_ Inferential statistics_en.srt - 15.3 KB
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006 MATLAB_ Inferential statistics_en.vtt - 13.35 KB
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007 Python_ Import and clean the marriage data.mp4 - 249.82 MB
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007 Python_ Import and clean the marriage data_en.srt - 29.28 KB
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007 Python_ Import and clean the marriage data_en.vtt - 25.48 KB
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008 Python_ Import the divorce data.mp4 - 137.14 MB
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008 Python_ Import the divorce data_en.srt - 18.53 KB
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008 Python_ Import the divorce data_en.vtt - 16.08 KB
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009 Python_ Inferential statistics.mp4 - 115.54 MB
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009 Python_ Inferential statistics_en.srt - 16.08 KB
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009 Python_ Inferential statistics_en.vtt - 14.05 KB
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010 Take-home messages.mp4 - 43.8 MB
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010 Take-home messages_en.srt - 8.72 KB
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010 Take-home messages_en.vtt - 7.6 KB
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35855730-state-marriage-rates-90-95-99-19.xlsx - 23.64 KB
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35855734-state-divorce-rates-90-95-99-19.xlsx - 22.47 KB
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19 - Bonus section
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001 About deep learning.html - 619 B
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002 Bonus content.html - 3.64 KB
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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InfoHash:
4C7F614ECF69937C739C2FDBA84CF93F73205B17
Last updated:
Aug 10, 2023
File count:
682 B
File Category:
9
Tags:
code, math, intuition, learning, machine, statistics, Master, Udemy, Other, Tutorials