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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.
Learn Principal Component Analysis and Singular Value Decomposition with StatQuest in less than an hour. Understand complex data patterns and variable importance.
Learn linear regression concepts, fitting a line to data, and R-squared in under an hour with StatQuest's Josh Starmer. Includes companion video and example code.
Learn to filter genes with low read counts using edgeR and DESeq2 in less than an hour with StatQuest's Josh Starmer.
Learn to interpret PCA plots and extract key information from RNA-seq results in under an hour with StatQuest's step-by-step guide, including R code examples.
Learn to create and optimize neural networks using PyTorch with StatQuest's under 1-hour tutorial. Assumes familiarity with neural networks and backpropagation.
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.
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