I2ML - Tuning - In a Nutshell

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  1. Statistical Learning and Data Science
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I2ML - Tuning 0:10:55

I2ML - Tuning - In a Nutshell

I2ML - Tuning 0:09:49

I2ML - Tuning - Intro

I2ML - Tuning 0:37:59

I2ML - Tuning - Advanced tuning techniques

I2ML - Tuning 0:11:52

I2ML - Tuning - Basic Techniques

I2ML - Tuning 0:13:41

I2ML - Tuning - Problem definition

I2ML - Tuning 0:20:02

I2ML - Tuning - Pipelines and AutoML

I2ML - ML 0:10:57

I2ML - ML Basics - Models & Parameters

I2ML - ML 0:14:31

I2ML - ML Basics - Optimization

I2ML - Nested 0:04:39

I2ML - Nested Resampling - Training - Validation - Testing

I2ML - Nested 0:07:06

I2ML - Nested Resampling

I2ML - CART 0:13:09

I2ML - CART - Stopping criteria & pruning

I2ML - Advanced 0:12:33

I2ML - Advanced Risk Minimization - Advanced Classification Losses

Model Selection & 0:10:29

Model Selection & Hyper-Parameter Tuning

I2ML - ML 0:18:59

I2ML - ML Basics - Data

I2ML - ML 0:13:16

I2ML - ML Basics - Components of Supervised Learning

I2ML - Nested 0:12:37

I2ML - Nested Resampling - Motivation

I2ML - Boosting 0:15:27

I2ML - Boosting - XGBoost

I2ML - Random 0:09:39

I2ML - Random Forest - Introduction

I2ML - CART 0:06:03

I2ML - CART - Growing a tree

I2ML - Regularization 0:23:34

I2ML - Regularization - Nonlinear & Bayesian Regularization

Model Parameters vs 1:08:05

Model Parameters vs Hyperparameters - Techniques in ML Engineering #machinelearning

I2ML - Boosting 0:23:44

I2ML - Boosting - Adaboost

I2ML - Random 0:08:45

I2ML - Random Forest - Benchmarking Trees, Forests, and Bagging K-NN

I2ML - Random 0:07:27

I2ML - Random Forest - Advantages & Disadvantages

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Really enjoyed the guitar tuning analogy. Can’t stand playing out of tune guitars or when the intonation is slightly off.

kevon