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Master Complete Statistics For Computer Science - II

Master Complete Statistics For Computer Science - II
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 10.1 GB
Genre: eLearning Video | Duration: 168 lectures (25 hour, 36 mins) | Language: English

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 experiment behind each of these distributions. Since these random experiments 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:
Introduction
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
Who this course is for:
Current Probability and Statistics students
Students of Machine Learning, Data Science, Computer Science, Electrical Engineering , as Statistics is the prerequisite course to Machine Learning, Data Science, Computer Science and Electrical Engineering
Anyone who wants to study Statistics for fun after being away from school for a while.

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Master Complete Statistics For Computer Science - II

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