Last updated 11/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 10.09 GB | Duration: 25h 36m
Course In Probability & Statistics Important For Machine Learning, Artificial Intelligence, Data Science, Neural Network What you'll learn Binomial Distribution Poisson Distribution Geometric Distribution Hypergeometric Distribution Uniform or Rectangular Distribution Exponential or Negative Exponential Distribution Erlang or General Gamma Distribution Weibull Distribution Normal or Gaussian Distribution Central Limit Theorem Hypotheses Testing Large Sample Test - Tests of Significance for Large Samples Small Sample Test - Tests of Significance for Small Samples Chi - Square Test - Test of Goodness of Fit Requirements Knowledge of Applied Probability Knowledge of Master Complete Statistics For Computer Science - I Knowledge of Calculus Description As it turns out, there are some specific distributions that are used over and over in practice for e.g. Normal Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution etc.There is a random expent behind each of these distributions. Since these random expents model a lot of real life phenomenon, these special distribution are used in different applications like Machine Learning, Neural Network, Data Science etc. That is why they have been given a special names and we devote a course "Master Complete Statistics For Computer Science - II" to study them. After learning about special probability distribution, the second half of this course is devoted for data analysis through inferential statistics which is also referred to as statistical inference.Technically speaking, the methods of statistical inference help in generalizing the results of a sample to the entire population from which the sample is drawn.This 150+ lecture course includes video explanations of everything from Special Probability Distributions and Sampling Distribution, and it includes more than 85+ examples (with detailed solutions) to help you test your understanding along the way. "Master Complete Statistics For Computer Science - II" is organized into the following sections:IntroductionBinomial DistributionPoisson DistributionGeometric DistributionHypergeometric DistributionUniform or Rectangular DistributionExponential or Negative Exponential DistributionErlang or General Gamma DistributionWeibull DistributionNormal or Gaussian Distribution Central Limit TheoremHypotheses TestingLarge Sample Test - Tests of Significance for Large SamplesSmall Sample Test - Tests of Significance for Small SamplesChi - Square Test - Test of Goodness of Fit Who this course is for Current Probability and Statistics students,Students of Machine Learning, Data Science, Computer Science, Electrical Eeering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Eeering,Anyone who wants to study Statistics for fun after being away from school for a while. HomePage: gfxtra_.MasterComp.part01.rar.html gfxtra_.MasterComp.part02.rar.html gfxtra_.MasterComp.part03.rar.html gfxtra_.MasterComp.part04.rar.html gfxtra_.MasterComp.part05.rar.html gfxtra_.MasterComp.part06.rar.html gfxtra_.MasterComp.part07.rar.html gfxtra_.MasterComp.part08.rar.html gfxtra_.MasterComp.part09.rar.html 
TO MAC USERS: If RAR password doesn't work, use this archive program:
RAR Expander 0.8.5 Beta 4 and extract password protected files without error.
TO WIN USERS: If RAR password doesn't work, use this archive program:
Latest Winrar and extract password protected files without error.
Themelli |
