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Learn to derive the Cross Entropy function for Neural Networks and apply it for Backpropagation in less than an hour with StatQuest's guide.
Dive into XGBoost in Python with StatQuest's under 1-hour program. Learn to import data, handle missing data, format data, build and optimize XGBoost models.
Learn to implement Support Vector Machines in Python with StatQuest's concise, under 1-hour material. Covers data importing, handling missing data, downsampling, data formatting, and optimization.
Learn to build and prune classification trees in Python with StatQuest's 1-2 hour material, covering data formatting, handling missing data, and visualizing alpha.
Learn to calculate p-values using discrete and continuous data in less than an hour with StatQuest's Josh Starmer. Understand the difference between 1 and 2-sided p-values.
Dive into advanced XGBoost optimizations with StatQuest's under 1-hour material. Learn about Approximate Greedy Algorithm, Parallel Learning, Sparsity-Aware Split Finding, and more.
Dive into XGBoost trees for classification with StatQuest's under 1-hour material. Assumes familiarity with XGBoost Part 1, Gradient Boost, Odds, and Logistic Function.
Dive into XGBoost's unique regression trees with StatQuest's under 1-hour material, assuming prior knowledge of Gradient Boost for Regression and Regularization.
Unravel the mystery of Support Vector Machines in less than an hour with StatQuest's online material, covering basic concepts, kernel functions, and more.
Learn the fundamentals of Regression Trees in machine learning with StatQuest's concise, less than 1-hour program. Ideal for those familiar with the bias/variance tradeoff, Decision Trees, and Linear Regression.
Understand the concept of covariance with StatQuest's short, engaging video. Learn its computation, uses, and its role as a stepping stone to other statistical concepts.
Dive into Gradient Boost algorithm for regression with StatQuest's under 1-hour video tutorial, focusing on predicting continuous values. Prior knowledge of Part 1 and Regression Trees required.
Learn the fundamentals of Gradient Descent in Machine Learning with StatQuest's step-by-step video guide. Ideal for those familiar with Least Squares and Linear Regression.
Learn AdaBoost, a machine learning method, in less than an hour with StatQuest. The material simplifies decision trees and random forests, assuming prior knowledge of these topics.
Learn Ridge Regression with StatQuest in under an hour, preventing overfitting and solving unsolvable equations in your data model.
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