Dive into a comprehensive video series covering a wide range of statistical concepts and machine learning techniques. Learn about principal component analysis, logistic regression, decision trees, random forests, support vector machines, and neural networks. Explore fundamental statistical concepts like probability, likelihood, p-values, and confidence intervals. Gain insights into data visualization techniques, including histograms, heatmaps, and boxplots. Understand various regression methods, clustering algorithms, and dimensionality reduction techniques. Discover practical applications in R and Python, and delve into specialized topics like RNA-seq analysis and ChIP-Seq. Master the essentials of hypothesis testing, experimental design, and data interpretation to become proficient in statistical analysis and machine learning.
Overview
Syllabus
StatQuest: Principal Component Analysis (PCA), Step-by-Step.
StatQuest: Logistic Regression.
Probability is not Likelihood. Find out why!!!.
Maximum Likelihood, clearly explained!!!.
StatQuest: P Values, clearly explained.
StatQuest: PCA main ideas in only 5 minutes!!!.
StatQuest: Decision Trees.
Machine Learning Fundamentals: Bias and Variance.
StatQuest: K-means clustering.
StatQuest: Random Forests Part 1 - Building, Using and Evaluating.
ROC and AUC, Clearly Explained!.
Regularization Part 1: Ridge (L2) Regression.
Support Vector Machines Part 1 (of 3): Main Ideas!!!.
Gradient Descent, Step-by-Step.
Logistic Regression Details Pt1: Coefficients.
StatQuest: Linear Discriminant Analysis (LDA) clearly explained..
Machine Learning Fundamentals: Cross Validation.
Linear Regression, Clearly Explained!!!.
A Gentle Introduction to Machine Learning.
AdaBoost, Clearly Explained.
StatQuest: A gentle introduction to RNA-seq.
Gradient Boost Part 1 (of 4): Regression Main Ideas.
Maximum Likelihood For the Normal Distribution, step-by-step!!!.
Logistic Regression in R, Clearly Explained!!!!.
Quantile-Quantile Plots (QQ plots), Clearly Explained!!!.
p-values: What they are and how to interpret them.
StatQuest: t-SNE, Clearly Explained.
Regularization Part 2: Lasso (L1) Regression.
Machine Learning Fundamentals: The Confusion Matrix.
R-squared, Clearly Explained!!!.
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.).
Covariance, Clearly Explained!!!.
Logistic Regression Details Pt 2: Maximum Likelihood.
The Normal Distribution, Clearly Explained!!!.
StatQuest: Histograms, Clearly Explained.
StatQuest: K-nearest neighbors, Clearly Explained.
Standard Deviation vs Standard Error, Clearly Explained!!!.
Stochastic Gradient Descent, Clearly Explained!!!.
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!.
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!.
StatQuest: Hierarchical Clustering.
Multiple Regression, Clearly Explained!!!.
Quantiles and Percentiles, Clearly Explained!!!.
StatQuest: PCA in R.
Regression Trees, Clearly Explained!!!.
RPKM, FPKM and TPM, Clearly Explained!!!.
XGBoost Part 1 (of 4): Regression.
Naive Bayes, Clearly Explained!!!.
Logistic Regression Details Pt 3: R-squared and p-value.
The Main Ideas behind Probability Distributions.
Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!.
ROC and AUC in R.
The Binomial Distribution and Test, Clearly Explained!!!.
Gradient Boost Part 2 (of 4): Regression Details.
StatQuest: PCA in Python.
StatQuest: MDS and PCoA.
Pearson's Correlation, Clearly Explained!!!.
Regularization Part 3: Elastic Net Regression.
Gradient Boost Part 3 (of 4): Classification.
StatQuest: A gentle introduction to ChIP-Seq.
How to calculate p-values.
The standard error, Clearly Explained!!!.
What is a (mathematical) model?.
Machine Learning Fundamentals: Sensitivity and Specificity.
Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!.
Machine Learning Fundamentals: Sensitivity and Specificity (old version).
Population and Estimated Parameters, Clearly Explained!!!.
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3).
Confidence Intervals, Clearly Explained!!!.
StatQuest: Random Forests in R.
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data.
Sampling from a Distribution, Clearly Explained!!!.
Multiple Regression in R, Step-by-Step!!!.
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3).
Ridge, Lasso and Elastic-Net Regression in R.
Lowess and Loess, Clearly Explained!!!.
StatQuest: PCA - Practical Tips.
Linear Regression in R, Step-by-Step.
Logs (logarithms), Clearly Explained!!!.
Drawing and Interpreting Heatmaps.
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!.
StatQuest: DESeq2, part 1, Library Normalization.
Sample Size and Effective Sample Size, Clearly Explained!!!.
Saturated Models and Deviance.
XGBoost Part 2 (of 4): Classification.
Gaussian Naive Bayes, Clearly Explained!!!.
How to Prune Regression Trees, Clearly Explained!!!.
Gradient Boost Part 4 (of 4): Classification Details.
Why Dividing By N Underestimates the Variance.
StatQuest: Random Forests Part 2: Missing data and clustering.
Fisher's Exact Test and the Hypergeometric Distribution.
Deviance Residuals.
Boxplots are Awesome!!!.
Ridge vs Lasso Regression, Visualized!!!.
Design Matrices For Linear Models, Clearly Explained!!!.
Power Analysis, Clearly Explained!!!.
The Difference Between Technical and Biological Replicates.
Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!.
Statistical Power, Clearly Explained!!!.
StatQuest: The Trailer!.
Quantile Normalization, Clearly Explained!!!.
p-hacking and power calculations.
StatQuest: One or Two Tailed P-Values.
XGBoost Part 3 (of 4): Mathematical Details.
StatQuest: edgeR and DESeq2, part 2 - Independent Filtering.
Design Matrix Examples in R, Clearly Explained!!!.
Neural Networks Pt. 1: Inside the Black Box.
StatQuickie: Which t test to use.
p-hacking: What it is and how to avoid it!.
StatQuest: MDS and PCoA in R.
Live 2020-03-16!!! Naive Bayes.
XGBoost Part 4 (of 4): Crazy Cool Optimizations.
Bam!!! Clearly Explained!!!.
StatQuest: edgeR, part 1, Library Normalization.
StatQuickie: Thresholds for Significance.
Bar Charts Are Better than Pie Charts.
Alternative Hypotheses: Main Ideas!!!.
The Chain Rule.
Live 2020-04-06!!! Naive Bayes: Gaussian.
Live 2020-04-20!!! Expected Values.
StatQuest: RNA-seq - the problem with technical replicates.
Neural Networks Pt. 2: Backpropagation Main Ideas.
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously..
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule.
Neural Networks Pt. 3: ReLU In Action!!!.
Neural Networks Pt. 4: Multiple Inputs and Outputs.
StatQuest: How to make a Mean Pizza Crust!!!.
US Census Data and Contest!!!.
Neural Networks Part 5: ArgMax and SoftMax.
The SoftMax Derivative, Step-by-Step!!!.
Neural Networks Part 6: Cross Entropy.
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation.
Neural Networks Part 8: Image Classification with Convolutional Neural Networks.
Silly Songs, Clearly Explained!!!.
Decision and Classification Trees, Clearly Explained!!!.
How to make your own StatQuest!!!.
Three (3) things to do when starting out in Data Science.
Ken Jee's #66DaysOfData Challenge Clearly Explained!!!.
Bootstrapping Main Ideas!!!.
Using Bootstrapping to Calculate p-values!!!.
Conditional Probabilities, Clearly Explained!!!.
Conditional Probabilities, Clearly Explained!!!.
Bayes' Theorem, Clearly Explained!!!!.
Entropy (for data science) Clearly Explained!!!.
Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!).
p-values: What they are and how to interpret them.
Clustering with DBSCAN, Clearly Explained!!!.
Tensors for Neural Networks, Clearly Explained!!!.
Troll 2, Clearly Explained!!!.
Taught by
StatQuest with Josh Starmer
