Intro to Probability and Statistics
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Course Moved - Click For Link
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Course Intro, Motivation, Methodology (8:51)
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Code Env Setup, First Look at Data and Visualization (30:59)
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Basics of Stats, Sources and Types of Data, One-Hot Vector Encoding (34:25)
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Data Types Continued. Visualizations, Histograms (21:42)
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Central Tendency, Averages, Median, Quartiles, Boxplots (29:21)
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Dispersion, Variance, Standard Deviation, More Visualizations (45:06)
Probability Basics
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Quantifying Chances, Entropy and Information; Motivation (17:26)
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Probability, Events, Combining Events, Rationale and Scientific Discovery (50:01)
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Simulating Coin Flips, Probability Code, Reproducible Experiments (29:48)
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Rules of Probability, Addition, Product, Conditional Probability (31:10)
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Application of Conditional Probability, Bayes Rule, Rationale and Importance (28:49)
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Spam Detection Maths - Bayes Rule Real World (11:20)
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Real World Bayes Rule Case Study - Spam Detection Code (27:33)
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Random Variables, Basics and Rationale (19:11)
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Joint and Marginal Probabilities (25:41)
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Distributions Introduction, Discrete Distributions, PMF (37:45)
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Discrete Distributions in Code, Visualization (7:38)
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Continuous Distributions, Uniform, Normal, PDF (43:23)
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Continuous Distributions Code, Sleep Analysis Case Study (22:20)
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Higher Dimentional Joints, Visualization, Contour Plots, Dependence (44:43)
Applied Probability -- Closer to the Real World
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Expectation, Expected Values, Rationale and Importance (15:13)
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Information, Entropy, Order and Disorder, Application Areas (46:59)
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Entropy Application - Machine Learning with Decision Trees (24:56)
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Comparing Distributions, Divergence, KL-Divergence (12:27)
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What's Wrong with Point Estimates; Bayesian Statistics, MCMC, PyMC3 (27:07)
