Master Complete Statistics For Computer Science - II

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.