Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
More Standard Deviation and Variance
Further explanations and examples of standard deviation and variance
From playlist Unit 1: Descriptive Statistics
Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
https://www.patreon.com/ProfessorLeonard Statistics Lecture 6.3: Applications of the Standard Normal Distribution. Using z-score, Standard Score
From playlist Statistics (Full Length Videos)
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Linear Regression using Python
This seminar series looks at four important linear models (linear regression, analysis of variance, analysis of covariance, and logistic regression). A video that explains all four model types is at https://www.youtube.com/watch?v=SV9AxXFWZnM&t=12s This video is on linear regression usin
From playlist Statistics
(ML 16.7) EM for the Gaussian mixture model (part 1)
Applying EM (Expectation-Maximization) to estimate the parameters of a Gaussian mixture model. Here we use the alternate formulation presented for (unconstrained) exponential families.
From playlist Machine Learning
Data that are collected for statistical analysis can be classified according to their type. It is important to know what data type we are dealing with as this determines the type of statistical test to use.
From playlist Learning medical statistics with python and Jupyter notebooks
(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
https://www.patreon.com/ProfessorLeonard Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
From playlist Statistics (Full Length Videos)
02.10 - ISE2021 - Language Model and N-Grams - 1
Information Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 4: Natural Language Processing - 3 2.10 Language Model and N-Grams - 1 - How to predict a word? - N-grams - Statistical language models - Bayes Theorem Playlist: htt
From playlist ISE 2021 - Lecture 04, 05.05.2021
Gussow2018 - Unconventional Reservoir Uncertainty
My talk from Gussow 2018 Conference in Lake Louise, Alberta, Canada. I recorded the talk afterwards, with added references and a little more time to explain all the topics.
From playlist Random Talks
Efficiency is the New Precision | NLP Summit 2020
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all NLP Summit 2020 sessions: https://www.nlpsummit.org/ The global data sphere consisting of machine data and h
From playlist NLP Summit 2020
Kaggle Reading Group: Probing Neural Network Comprehension of Natural Language Arguments (Part 2)
BERT (which we read the paper for earlier) has had really impressive success on a number of NLP tasks... but how well is it really capturing the structures of natural language? This week we're continuing with "Probing Neural Network Comprehension of Natural Language Arguments" (Niven & K
From playlist Kaggle Reading Group | Kaggle
Sophie Achard: Statistical comparisons of spatio-temporal networks
CONFERENCE Recording during the thematic meeting : " Machine Learning and Signal Processing on Graphs" the November 7, 2022 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide math
From playlist Probability and Statistics
Why The Best Data Scientists have Mastered Algebra, Calculus and Probability
All the outstanding data scientist and ML engineers have one thing in common: They have a strong, working understanding of how ML's high-level software libraries work. Being able to look under the hood, and understand what's going in libraries such as scikit-learn, TensorFlow, and Keras,
From playlist Talks and Tutorials
09b Data Analytics: Linear Regression
A practical lecture on linear regression and how to do it in Excel and R.
From playlist Data Analytics and Geostatistics
Sylvia Frühwirth-Schnatter: Bayesian econometrics in the Big Data Era
Abstract: Data mining methods based on finite mixture models are quite common in many areas of applied science, such as marketing, to segment data and to identify subgroups with specific features. Recent work shows that these methods are also useful in micro econometrics to analyze the beh
From playlist Probability and Statistics
18 Geostatistics Course: Machine Learning
Lecture with introduction and basic concepts related to machine learning.
From playlist Data Analytics and Geostatistics
Data Science - Part IV - Regression Analysis and ANOVA Concepts
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-leve
From playlist Data Science
From playlist STAT 501