Advanced Predictive Modelling in R | Predictive Modelling Techniques | What is Predictive Modelling
Watch Sample Recording : http://www.edureka.co/about-advanced-predictive-modelling-in-r?utm_source=youtube&utm_medium=referral&utm_campaign=apmr-what-is-pred-mod Predictive modelling leverages statistics to predict outcomes.[1] Most often the event one wants to predict is in the future, b
From playlist Advanced Predictive Modelling in R Tutorial Videos
(ML 10.7) Predictive distribution for linear regression (part 4)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
Model-Based Design for Predictive Maintenance, Part 5: Development of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 After performing real-time tests and validating your algorithm, you can use it to detect whether there are any mechanical or electrical issues in your system. However, you can also use condition
From playlist Model-Based Design for Predictive Maintenance
(ML 10.5) Predictive distribution for linear regression (part 2)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
(ML 10.6) Predictive distribution for linear regression (part 3)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
(ML 10.4) Predictive distribution for linear regression (part 1)
How to compute the (posterior) predictive distribution for a new point, under a Bayesian model for linear regression.
From playlist Machine Learning
Introduction to Classification | Predictive Modeling and Machine Learning, Part 2
This video covers the basics of the most common machine learning classification models that you can tune to work with any number of predictor variables. Each has its advantages and disadvantages in terms of accuracy and training speed. The only way to know which one works best on a particu
From playlist Predictive Modeling and Machine Learning
Aki Vehtari: Model assessment, selection and averaging
Abstract: The tutorial covers cross-validation, and projection predictive approaches for model assessment, selection and inference after model selection and Bayesian stacking for model averaging. The talk is accompanied with R notebooks using rstanarm, bayesplot, loo, and projpred packages
From playlist Probability and Statistics
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From playlist CS294-112 Deep Reinforcement Learning Sp17
This is Lecture 14 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www
From playlist CSE519 - Data Science Fall 2016
Python for Data Analysis: Logistic Regression
This video covers the basics of logistic regression and how to perform logistic regression in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 28 of a 30-part introduction to the Python programming language for data analysis and predictive modelin
From playlist Python for Data Analysis
Statistical Rethinking 2022 Lecture 07 - Overfitting
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music: Intro: https://www.youtube.com/watch?v=R9bwnY05GoU Pause: https://www.youtube.com/watch?v=wAPCSnAhhC8 Chapters: 00:00 Introduction 04:26 Problems of prediction 07:00 Cross-validation 22:00 Regula
From playlist Statistical Rethinking 2022
The Whys and Hows of Predictive Modelling | Webinar - 1 | Edureka
Watch Sample Recording : http://www.edureka.co/about-advanced-predictive-modelling-in-r?utm_source=youtube&utm_medium=webinar&utm_campaign=apmr-19-03-2015 Predictive modelling leverages statistics to predict outcomes.[1] Most often the event one wants to predict is in the future, but pred
From playlist Webinars by Edureka!
Introduction to R: Linear Regression
This lesson covers the basics of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models. This is lesson 27 of a 30-
From playlist Introduction to R
Introduction to R: Logistic Regression
This lesson covers the basics of logistic regression in R. This is lesson 28 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Logistic Regression https://www.kaggle.com/hamelg/intro-to-r-part-2
From playlist Introduction to R
Model-Based Design for Predictive Maintenance, Part 6: Deployment of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 This video shows how prognostics models work, how they perform, and how you can deploy them. You’ll learn how to deploy a remaining useful life estimation model either as a standalone applicatio
From playlist Model-Based Design for Predictive Maintenance
How to evaluate a classifier in scikit-learn
In this video, you'll learn how to properly evaluate a classification model using a variety of common tools and metrics, as well as how to adjust the performance of a classifier to best match your business objectives. I'll start by demonstrating the weaknesses of classification accuracy as
From playlist Machine learning in Python with scikit-learn