Introduction to Regression Analysis
This video introduced analysis and discusses how to determine if a given regression equation is a good model using r and r^2.
From playlist Performing Linear Regression and Correlation
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
Can You Validate These Emails?
Email Validation is a procedure that verifies if an email address is deliverable and valid. Can you validate these emails?
From playlist Fun
What are the different ways to perform data validation in machine learning?
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From playlist Quick Machine Learning Concepts
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
Regression Trees, Clearly Explained!!!
Regression Trees are one of the fundamental machine learning techniques that more complicated methods, like Gradient Boost, are based on. They are useful for times when there isn't an obviously linear relationship between what you want to predict, and the things you are using to make the p
From playlist StatQuest
Regression: Model Selection and Validation, Part 1
Data Science for Biologists Regression: Model Selection and Validation Part 1 Course Website: data4bio.com Instructors: Nathan Kutz: faculty.washington.edu/kutz Bing Brunton: faculty.washington.edu/bbrunton Steve Brunton: faculty.washington.edu/sbrunton
From playlist Data Science for Biologists
Frank Noé: "Intro to Machine Learning (Part 1/2)"
Watch part 2/2 here: https://youtu.be/7TZnGQrNF6g Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Intro to Machine Learning (Part 1/2)" Frank Noé, Freie Universität Berlin Institute for Pure and Applied Mathematics, UCLA September 5, 2019 For more information:
From playlist Machine Learning for Physics and the Physics of Learning 2019
Deep Learning Lecture 1.2 - Intro Shallow ML
Deep Learning Lecture Intro: - Shallow ML - Learning Problem - Linear Least Squares - Hyperparameter Selection
From playlist Deep Learning Lecture
Assumptions and Conditions for Regression
From playlist Regression Analysis
Frank Noé: "Fundamentals of Artificial Intelligence and Machine Learning" (Part 1/2)
Watch part 2/2 here: https://youtu.be/gSLQ_2uFSiA Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Fundamentals of Artificial Intelligence and Machine Learning" (Part 1/2) Frank Noé - Freie Universität Berlin Institute for Pure and Applied Mathematics, U
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Statistical Learning: 6.R.4 Ridge Regression and Lasso
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
From playlist Thinking about Data
Regularization Part 1: Ridge (L2) Regression
Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizing your model to the training data. It can also help you solve unsolvable equations, and if that isn't bad to the bone, I don't know what is. This StatQuest follows up on
From playlist StatQuest
Applied ML 2020 - 10 - Calibration, Imbalanced data
Class materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020
Selecting the best model in scikit-learn using cross-validation
In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validation with the train/test split procedure, and we'll also discuss some variations of cross-vali
From playlist Machine learning in Python with scikit-learn
Koopman Spectral Analysis (Representations)
In this video, we explore how to obtain finite-dimensional representations of the Koopman operator from data, using regression. This includes the use of sparse regression and neural networks, and highlights the importance of cross-validating. https://www.eigensteve.com/
From playlist Koopman Analysis
Applied Machine Learning 2019 - Lecture 09 - Gradient boosting; Calibration
Gradient boosting and "extreme" gradient boosting Calibration curves and calibrating classifiers with CalibratedClassifierCV. Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019