StatQuest with Josh Starmer Courses

Transformer Neural Networks, ChatGPT's Foundation, Clearly Explained

Clear explanation of Transformer Neural Networks, the foundation of ChatGPT and modern AI. Covers key concepts like word embedding, self-attention, and encoder-decoder architecture.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Decoder-Only Transformers, ChatGPT's Specific Transformer, Clearly Explained

Comprehensive explanation of Decoder-Only Transformers used in ChatGPT, covering word embedding, position encoding, masked self-attention, and output generation, with comparisons to normal Transformers.

• YouTube
• 37 minutes
• On-Demand
• Free Video

XGBoost in Python from Start to Finish

Comprehensive guide to implementing XGBoost in Python, covering data preparation, model building, and optimization techniques for effective machine learning applications.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Support Vector Machines in Python from Start to Finish

Comprehensive guide to implementing Support Vector Machines in Python, covering data preparation, model building, and optimization techniques for machine learning practitioners.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Classification Trees in Python from Start to Finish

Comprehensive guide to building and optimizing classification trees in Python, covering data preparation, model construction, pruning techniques, and visualization.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

How to Calculate P-Values

Learn to calculate p-values for discrete and continuous data, understand one-sided vs two-sided p-values, and gain practical insights for statistical analysis in this comprehensive tutorial.

• YouTube
• 25 minutes
• On-Demand
• Free Video

XGBoost Part 4 - Crazy Cool Optimizations

Explore advanced XGBoost optimizations for large datasets, including approximate algorithms, parallel learning, and sparsity-aware techniques to enhance machine learning efficiency.

• YouTube
• 24 minutes
• On-Demand
• Free Video

XGBoost Part 2 - Classification

Learn how XGBoost trees are built for classification, covering similarity scores, gain, cover, pruning, and logistic regression in this advanced machine learning tutorial.

• YouTube
• 25 minutes
• On-Demand
• Free Video

XGBoost Part 1 - Regression

Explore XGBoost's unique regression trees, focusing on similarity scores, gain calculation, pruning, regularization, and making predictions in this comprehensive tutorial.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Support Vector Machines Part 1 - Main Ideas

Demystifying Support Vector Machines: Learn key concepts, from maximal margin classifiers to kernel functions, in this comprehensive introduction to SVM fundamentals and applications.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Regression Trees, Clearly Explained

Clear explanation of regression trees, their applications, and step-by-step construction process. Covers differences from classification trees and use with multiple variables.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Covariance, Clearly Explained

Demystifies covariance, its calculation, and significance in statistics. Explores relationships between variables and sets the stage for understanding correlation.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Gradient Boost Part 2 - Regression Details

Detailed exploration of Gradient Boost algorithm for regression, covering initialization, tree building, residual calculation, and prediction optimization. Ideal for those seeking in-depth understanding of this popular machine learning technique.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Gradient Descent, Step-by-Step

Comprehensive step-by-step explanation of Gradient Descent, covering its application in Machine Learning, optimization techniques, and variations like Stochastic Gradient Descent.

• YouTube
• 24 minutes
• On-Demand
• Free Video

AdaBoost, Clearly Explained

Clear explanation of AdaBoost algorithm, covering key concepts like decision stumps, sample weighting, and ensemble learning. Ideal for those familiar with decision trees and random forests.

• YouTube
• 21 minutes
• On-Demand
• Free Video