Summaries of StatQuest with Josh Starmer Videos 📚

Regularization Part 3: Elastic Net Regression

Recurrent Neural Networks (RNNs), Clearly Explained!!!

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!

StatQuest: PCA main ideas in only 5 minutes!!!

Clustering with DBSCAN, Clearly Explained!!!

Neural Networks Part 6: Cross Entropy

Bayes' Theorem, Clearly Explained!!!!

Alternative Hypotheses: Main Ideas!!!

Silly Songs, Clearly Explained!!!

XGBoost Part 1 (of 4): Regression

The matrix math behind transformer neural networks, one step at a time!!!

Design Matrices For Linear Models, Clearly Explained!!!

R-squared, Clearly Explained!!!

Live 2020-05-18!!! Bayes' Theorem

R-squared, Clearly Explained!!!

Confidence Intervals, Clearly Explained!!!

The Cosine Similarity for NLP and CatBoost

StatQuest: DESeq2, part 1, Library Normalization

The standard error, Clearly Explained!!!

StatQuest: One or Two Tailed P-Values

The Central Limit Theorem, Clearly Explained!!!

Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.

Essential Matrix Algebra for Neural Networks, Clearly Explained!!!

Gradient Boost Part 1 (of 4): Regression Main Ideas

Neural Networks Pt. 4: Multiple Inputs and Outputs

Gradient Boost Part 4 (of 4): Classification Details

A Drink From The Well

Design Matrix Examples in R, Clearly Explained!!!

The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)

Three more lessons from my Pop!!!

Attention for Neural Networks, Clearly Explained!!!

Handmade Pasta, Clearly Explained!!!

Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!

Lowess and Loess, Clearly Explained!!!

Multiple Regression in R, Step-by-Step!!!

Support Vector Machines Part 1 (of 3): Main Ideas!!!

StatQuest: edgeR and DESeq2, part 2 - Independent Filtering

Logs (logarithms), Clearly Explained!!!

Regression Trees, Clearly Explained!!!

Log_e Song - Official Lyric Video

UMAP Dimension Reduction, Main Ideas!!!

Machine Learning Fundamentals: Cross Validation

Entropy (for data science) Clearly Explained!!!

Using Bootstrapping to Calculate p-values!!!

Live 2020-05-04!!! Conditional Probability

Live 2020-02-17!!! Imbalanced Data and Post-Hoc Tests

Power Analysis, Clearly Explained!!!

Machine Learning Fundamentals: The Confusion Matrix

Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)

Gaussian Naive Bayes, Clearly Explained!!!

Long Short-Term Memory (LSTM), Clearly Explained

Maximum Likelihood, clearly explained!!!

Live 2020-06-15!!! Bootstrapping, Main Ideas

StatQuest: RNA-seq - the problem with technical replicates

Using Linear Models for t-tests and ANOVA, Clearly Explained!!!

StatQuickie: Thresholds for Significance

The Binomial Distribution and Test, Clearly Explained!!!

XGBoost Part 3 (of 4): Mathematical Details

AdaBoost, Clearly Explained

StatQuest: edgeR, part 1, Library Normalization

Ken Jee's #66DaysOfData Challenge Clearly Explained!!!

Design Matrix Examples in R, Clearly Explained!!!

Live Stream - More details about Target Encoding/AMA/Silly Songs

Live Stream - Target Encoding/AMA/Silly Songs!!!

Decision and Classification Trees, Clearly Explained!!!

Live 2020-04-20!!! Expected Values

Odds and Log(Odds), Clearly Explained!!!

Mutual Information, Clearly Explained!!!

p-hacking and power calculations

Cosine Similarity, Clearly Explained!!!

Three (3) things to do when starting out in Data Science

Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!

Multiple Regression in R, Step by Step!!!

Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)

Principal Component Analysis (PCA) clearly explained (2015)

Statistical Power, Clearly Explained!!!

Regularization Part 2: Lasso (L1) Regression

Word Embedding and Word2Vec, Clearly Explained!!!

StatQuest: K-nearest neighbors, Clearly Explained

The Elements of StatQuest (Webinar)

The Main Ideas behind Probability Distributions

Human Stories in AI: Achal Dixit

Sampling from a Distribution, Clearly Explained!!!

Human Stories in AI: Fabio Urbina

Linear Regression in R, Step by Step

Neural Networks Pt. 2: Backpropagation Main Ideas

Quantile-Quantile Plots (QQ plots), Clearly Explained!!!

Live 2020-01-06!!! Sample Sizes, ML vs Statistics and a Poem

How to make your own StatQuest!!!

Support Vector Machines in Python from Start to Finish.

XGBoost Part 2 (of 4): Classification

ROC and AUC in R

Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)

Troll 2, Clearly Explained!!!

Regularization Part 1: Ridge (L2) Regression

UMAP: Mathematical Details (clearly explained!!!)

StatQuest: A gentle introduction to ChIP-Seq

Saturated Models and Deviance

Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation

Another 3 lessons from my Pop!!!

p-values: What they are and how to interpret them

Expected Values, Main Ideas!!!

Logistic Regression Details Pt 3: R-squared and p-value

The AI Buzz, Episode #2: Big data, Reinforcement Learning and Aligning Models

False Discovery Rates, FDR, clearly explained

Long Short-Term Memory with PyTorch + Lightning

CatBoost Part 2: Building and Using Trees

Introduction to Coding Neural Networks with PyTorch and Lightning

Stochastic Gradient Descent, Clearly Explained!!!

A Gentle Introduction to Machine Learning

CatBoost Part 1: Ordered Target Encoding

StatQuest: Random Forests Part 1 - Building, Using and Evaluating

Ridge, Lasso and Elastic-Net Regression in R

Live 2020-03-16!!! Naive Bayes

StatQuest: Random Forests Part 2: Missing data and clustering

The Essential Main Ideas of Neural Networks

Maximum Likelihood For the Normal Distribution, step-by-step!!!

Multiple Regression, Clearly Explained!!!

Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!

p-hacking: What it is and how to avoid it!

The StatQuest Introduction to PyTorch

The AI Buzz, Episode #1: ChatGPT, Transformers and Attention

XGBoost in Python from Start to Finish

Logistic Regression Details Pt1: Coefficients

Live 2020-06-01!!! Hypothesis Testing

StatQuest: Hierarchical Clustering

Bootstrapping Main Ideas!!!

The Ukulele: Clearly Explained!!!

Quantile Normalization, Clearly Explained!!!

Why Dividing By N Underestimates the Variance

Tensors for Neural Networks, Clearly Explained!!!

StatQuest: Histograms, Clearly Explained

Human Stories in AI: Khushi Jain

StatQuest: PCA - Practical Tips

US Census Data and Contest!!!

Naive Bayes, Clearly Explained!!!

Machine Learning Fundamentals: Sensitivity and Specificity

Logistic Regression in R, Clearly Explained!!!!

Odds Ratios and Log(Odds Ratios), Clearly Explained!!!

Design Matrices For Linear Models, Clearly Explained!!!

Human Stories in AI: Brian [email protected]

Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!

How to Prune Regression Trees, Clearly Explained!!!

Live 2020-01-20!!! Favorite ML, Data Leakage, How to Learn ML

Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!

StatQuest: t-SNE, Clearly Explained

Human Stories in AI: Rick Marks

What is a (mathematical) model?

StatQuickie: Which t test to use

StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data

The AI Buzz, Episode #4: ChatGPT + Bing and How to start an AI company in 3 easy steps.

Linear Regression, Clearly Explained!!!

Pearson's Correlation, Clearly Explained!!!

Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!

Machine Learning Fundamentals: Bias and Variance

One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!

StatQuest: K-means clustering

Gradient Descent, Step-by-Step

Gradient Boost Part 3 (of 4): Classification

Linear Regression in R, Step-by-Step

StatQuest: Principal Component Analysis (PCA), Step-by-Step

Drawing and Interpreting Heatmaps

Backpropagation Details Pt. 2: Going bonkers with The Chain Rule

Classification Trees in Python from Start to Finish

Live 2020-03-02!!! Virus Models and p-hacking

Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

StatQuest: How to make a Mean Pizza Crust!!!

The Normal Distribution, Clearly Explained!!!

StatQuest: A gentle introduction to RNA-seq

ROC and AUC, Clearly Explained!

Gradient Boost Part 2 (of 4): Regression Details

XGBoost Part 4 (of 4): Crazy Cool Optimizations

Probability is not Likelihood. Find out why!!!

Live 2020-02-03!!! Statistical Models, Regularization, Best ML Algorithm.

Linear Regression, Clearly Explained!!!

RPKM, FPKM and TPM, Clearly Explained!!!

Last Night

StatQuest: MDS and PCoA

What is AutoML? A conversation with Gnosis Data Analysis

The AI Buzz, Episode #5: A new wave of AI-based products and the resurgence of personal applications

Neural Networks Pt. 3: ReLU In Action!!!

Neural Networks Part 5: ArgMax and SoftMax

Sample Size and Effective Sample Size, Clearly Explained!!!

The AI Buzz, Episode #3: Constitutional AI, Emergent Abilities and Foundation Models

StatQuest: Random Forests in R

Human Stories in AI: Simon Stochholm

Fisher's Exact Test and the Hypergeometric Distribution

Little Red Fiat

StatQuest: PCA in Python

Covariance, Clearly Explained!!!

Using Linear Models for t tests and ANOVA, Clearly Explained!!!

Live 2020-04-06!!! Naive Bayes: Gaussian

StatQuest: MDS and PCoA in R

The Difference Between Technical and Biological Replicates

The Chain Rule

StatQuest: Logistic Regression

Word Embedding in PyTorch + Lightning

Population and Estimated Parameters, Clearly Explained!!!

The SoftMax Derivative, Step-by-Step!!!

Quantiles and Percentiles, Clearly Explained!!!

Multiple Regression, Clearly Explained!!!

StatQuest: Linear Discriminant Analysis (LDA) clearly explained.

Deviance Residuals