Log-logistic distribution

Ex: Determine the Value of a Number on a Logarithmic Scale (Log Form)

This video explains how to determine the value of several numbers on a logarithmic scale scaled in logarithmic form. http://mathispower4u.com

From playlist Using the Definition of a Logarithm

The Normal Distribution (1 of 3: Introductory definition)

More resources available at www.misterwootube.com

From playlist The Normal Distribution

Inverse normal with Z Table

Determining values of a variable at a particular percentile in a normal distribution

From playlist Unit 2: Normal Distributions

(ML 15.4) Logistic regression (binary) - formalism

From playlist Machine Learning

Lect.3F: Log-Normal And Uniform Distributions

Lecture with Per B. Brockhoff. Chapters: 00:00 - The Log-Normal Distribution; 04:15 - Example 6; 07:00 - The Uniform Distribution; 08:00 - Example 7;

From playlist DTU: Introduction to Statistics | CosmoLearning.org

Logistic Growth Function and Differential Equations

This calculus video tutorial explains the concept behind the logistic growth model function which describes the limits of population growth. This shows you how to derive the general solution or logistic growth formula starting from a differential equation which describes the population gr

From playlist New Precalculus Video Playlist

Solving the Logarithmic Equation log(A) = log(B) - C*log(x) for A

Solving the Logarithmic Equation log(A) = log(B) - C*log(x) for A Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys

From playlist Logarithmic Equations

Logistic Regression

Overview of logistic regression, a statistical classification technique.

From playlist Machine Learning

What is a Sampling Distribution?

Intro to sampling distributions. What is a sampling distribution? What is the mean of the sampling distribution of the mean? Check out my e-book, Sampling in Statistics, which covers everything you need to know to find samples with more than 20 different techniques: https://prof-essa.creat

From playlist Probability Distributions

Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 17-erm for probabilistic classif.

Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/

From playlist Stanford EE104: Introduction to Machine Learning Full Course

Lecture 4 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to

From playlist Lecture Collection | Machine Learning

Stanford CS229: Machine Learning | Summer 2019 | Lecture 6 - Exponential Family & GLM

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Eb7mIi Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html

From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3o Anand Avati PhD Candidate and CS229 Head TA To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.h

From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

Statistical Rethinking Fall 2017 - week07 lecture12

Week 07, lecture 12 for Statistical Rethinking: A Bayesian Course with Examples in R and Stan, taught at MPI-EVA in Fall 2017. This lecture covers Chapter 10. Slides are available here: https://speakerdeck.com/rmcelreath Additional information on textbook and R package here: http://xce

From playlist Statistical Rethinking Fall 2017

Digging into Data: Supervised Classification with Logistic Regression and Naive Bayes

Our first lecture on classification, where we cover two linear methods.

From playlist Digging into Data

Gradient Descent - THE MATH YOU SHOULD KNOW

All the math you need to know about gradient descent for Logistic Regression. INTERESTING VIDEOS [1] Visualize Logistic Regression:https://www.youtube.com/watch?v=slBI5YuVUTM [2] Newton Raphson optimization for logistic regression: https://www.youtube.com/watch?v=YMJtsYIp4kg REFERENCES

From playlist Logistic Regression

Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2ZdTL4x Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta

From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

Statistical Learning: 4.8 Generalized Linear Models

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning

From playlist Statistical Learning

Machine learning - Logistic regression

Logistic regression: Optimization and Bayesian inference via Monte Carlo. Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas

From playlist Machine Learning 2013

What is a Unimodal Distribution?

Quick definition of a unimodal distribution and how it compares to a bimodal distribution and a multimodal distribution.

From playlist Probability Distributions