1
StatQuest: Principal Component Analysis (PCA), Step-by-Step
21:58
2018-04-02
2
StatQuest: Logistic Regression
8:48
2018-03-05
3
In Statistics, Probability is not Likelihood.
5:01
2018-03-09
4
Maximum Likelihood, clearly explained!!!
6:12
2017-07-31
5
Private video
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6
StatQuest: PCA main ideas in only 5 minutes!!!
6:05
2017-12-04
7
StatQuest: Decision Trees
17:22
2018-01-22
8
Machine Learning Fundamentals: Bias and Variance
6:36
2018-09-17
9
StatQuest: K-means clustering
8:30
2018-05-23
10
StatQuest: Random Forests Part 1 - Building, Using and Evaluating
9:54
2018-02-05
11
ROC and AUC, Clearly Explained!
16:17
2019-07-11
12
Regularization Part 1: Ridge (L2) Regression
20:27
2018-09-24
13
Support Vector Machines Part 1 (of 3): Main Ideas!!!
20:32
2019-09-30
14
Gradient Descent, Step-by-Step
23:54
2019-02-05
15
Logistic Regression Details Pt1: Coefficients
19:02
2018-06-04
16
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
15:12
2016-07-10
17
Machine Learning Fundamentals: Cross Validation
6:05
2018-04-24
18
Linear Regression, Clearly Explained!!!
27:27
2022-11-18
19
A Gentle Introduction to Machine Learning
12:45
2018-11-26
20
AdaBoost, Clearly Explained
20:54
2019-01-14
21
StatQuest: A gentle introduction to RNA-seq
18:26
2017-08-31
22
Gradient Boost Part 1 (of 4): Regression Main Ideas
15:52
2019-03-25
23
Maximum Likelihood For the Normal Distribution, step-by-step!!!
19:50
2018-09-10
24
Logistic Regression in R, Clearly Explained!!!!
17:15
2018-07-26
25
Quantile-Quantile Plots (QQ plots), Clearly Explained!!!
6:56
2017-11-13
26
p-values: What they are and how to interpret them
11:21
2020-03-23
27
StatQuest: t-SNE, Clearly Explained
11:48
2017-09-18
28
Regularization Part 2: Lasso (L1) Regression
8:19
2018-10-01
29
Machine Learning Fundamentals: The Confusion Matrix
7:13
2018-10-29
30
R-squared, Clearly Explained!!!
11:01
2015-02-03
31
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
9:22
2017-05-22
32
Covariance, Clearly Explained!!!
22:23
2019-07-29
33
Logistic Regression Details Pt 2: Maximum Likelihood
10:23
2018-06-11
34
The Normal Distribution, Clearly Explained!!!
5:13
2017-10-09
35
StatQuest: Histograms, Clearly Explained
3:42
2017-09-25
36
StatQuest: K-nearest neighbors, Clearly Explained
5:30
2017-06-26
37
Standard Deviation vs Standard Error, Clearly Explained!!!
2:52
2017-03-20
38
Stochastic Gradient Descent, Clearly Explained!!!
10:53
2019-05-13
39
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
16:20
2018-06-21
40
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
11:38
2017-08-07
41
StatQuest: Hierarchical Clustering
11:19
2017-06-20
42
Multiple Regression, Clearly Explained!!!
5:25
2017-10-30
43
Quantiles and Percentiles, Clearly Explained!!!
6:30
2017-11-06
44
StatQuest: PCA in R
8:57
2017-11-27
45
Regression Trees, Clearly Explained!!!
22:33
2019-08-20
46
RPKM, FPKM and TPM, Clearly Explained!!!
10:15
2015-07-09
47
XGBoost Part 1 (of 4): Regression
25:46
2019-12-16
48
Naive Bayes, Clearly Explained!!!
15:12
2020-06-03
49
Logistic Regression Details Pt 3: R-squared and p-value
15:25
2018-06-18
50
The Main Ideas behind Probability Distributions
5:15
2017-04-17
51
Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!
14:22
2019-07-15
52
ROC and AUC in R
15:13
2018-12-18
53
The Binomial Distribution and Test, Clearly Explained!!!
15:47
2018-08-06
54
Gradient Boost Part 2 (of 4): Regression Details
26:46
2019-04-01
55
StatQuest: PCA in Python
11:37
2018-01-08
56
StatQuest: MDS and PCoA
8:18
2017-12-11
57
Pearson's Correlation, Clearly Explained!!!
19:13
2019-08-05
58
Regularization Part 3: Elastic Net Regression
5:19
2018-10-08
59
Gradient Boost Part 3 (of 4): Classification
17:03
2019-04-08
60
StatQuest: A gentle introduction to ChIP-Seq
8:30
2018-04-16
61
How to calculate p-values
25:15
2020-03-23
62
The standard error, Clearly Explained!!!
11:44
2015-05-12
63
What is a (mathematical) model?
3:45
2017-07-17
64
Machine Learning Fundamentals: Sensitivity and Specificity
11:47
2019-12-02
65
Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!
9:39
2018-07-30
66
Machine Learning Fundamentals: Sensitivity and Specificity (old version)
11:47
2018-11-05
67
Population and Estimated Parameters, Clearly Explained!!!
14:31
2019-07-01
68
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
7:15
2019-11-04
69
Confidence Intervals, Clearly Explained!!!
6:42
2015-07-09
70
StatQuest: Random Forests in R
15:10
2018-02-26
71
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
5:16
2018-01-29
72
Sampling from a Distribution, Clearly Explained!!!
3:49
2017-05-08
73
Multiple Regression in R, Step-by-Step!!!
7:43
2017-10-30
74
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
15:52
2019-11-04
75
Ridge, Lasso and Elastic-Net Regression in R
17:51
2018-10-23
76
Lowess and Loess, Clearly Explained!!!
10:10
2017-06-05
77
StatQuest: PCA - Practical Tips
8:20
2018-04-09
78
Linear Regression in R, Step-by-Step
5:01
2017-07-25
79
Logs (logarithms), Clearly Explained!!!
15:37
2017-02-23
80
Drawing and Interpreting Heatmaps
16:49
2016-01-06
81
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
11:24
2018-08-13
82
StatQuest: DESeq2, part 1, Library Normalization
12:42
2017-03-27
83
Sample Size and Effective Sample Size, Clearly Explained!!!
6:33
2017-10-23
84
Saturated Models and Deviance
18:40
2018-07-09
85
XGBoost Part 2 (of 4): Classification
25:18
2020-01-13
86
Gaussian Naive Bayes, Clearly Explained!!!
9:26
2020-06-03
87
How to Prune Regression Trees, Clearly Explained!!!
16:15
2019-11-25
88
Gradient Boost Part 4 (of 4): Classification Details
37:00
2019-04-22
89
Why Dividing By N Underestimates the Variance
17:15
2019-07-15
90
StatQuest: Random Forests Part 2: Missing data and clustering
10:48
2026-06-08
91
Fisher's Exact Test and the Hypergeometric Distribution
5:15
2017-03-13
92
Deviance Residuals
6:18
2018-07-16
93
Boxplots are Awesome!!!
2:33
2017-07-11
94
Ridge vs Lasso Regression, Visualized!!!
9:06
2020-05-19
95
Design Matrices For Linear Models, Clearly Explained!!!
14:40
2019-01-08
96
Power Analysis, Clearly Explained!!!
16:45
2020-05-04
97
The Difference Between Technical and Biological Replicates
5:27
2017-10-09
98
Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!
14:41
2020-07-06
99
Statistical Power, Clearly Explained!!!
8:19
2020-05-04
100
StatQuest: The Trailer!
0:49
2017-06-05
101
Quantile Normalization, Clearly Explained!!!
4:52
2017-11-20
102
p-hacking and power calculations
19:12
2016-10-11
103
StatQuest: One or Two Tailed P-Values
7:06
2017-04-24
104
XGBoost Part 3 (of 4): Mathematical Details
27:24
2020-02-10
105
StatQuest: edgeR and DESeq2, part 2 - Independent Filtering
21:24
2017-05-16
106
Design Matrix Examples in R, Clearly Explained!!!
8:20
2017-10-03
107
The Essential Main Ideas of Neural Networks
18:54
2020-08-31
108
StatQuickie: Which t test to use
5:10
2017-03-06
109
p-hacking: What it is and how to avoid it!
13:45
2020-05-04
110
StatQuest: MDS and PCoA in R
7:45
2017-12-18
111
Live 2020-03-16!!! Naive Bayes
33:51
2020-03-16
112
XGBoost Part 4 (of 4): Crazy Cool Optimizations
24:27
2020-03-02
113
Bam!!! Clearly Explained!!!
2:49
2020-04-01
114
StatQuest: edgeR, part 1, Library Normalization
14:17
2017-04-03
115
StatQuickie: Thresholds for Significance
6:40
2017-02-22
116
Bar Charts Are Better than Pie Charts
1:45
2017-02-27
117
Alternative Hypotheses: Main Ideas!!!
9:50
2020-07-06
118
The Chain Rule, Clearly Explained!!!
18:24
2020-07-13
119
Live 2020-04-06!!! Naive Bayes: Gaussian
36:41
2020-04-06
120
Live 2020-04-20!!! Expected Values
33:00
2020-04-20
121
StatQuest: RNA-seq - the problem with technical replicates
12:55
2015-08-27
122
Neural Networks Pt. 2: Backpropagation Main Ideas
17:34
2020-10-19
123
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
18:32
2020-11-02
124
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
13:09
2020-11-02
125
Neural Networks Pt. 3: ReLU In Action!!!
8:58
2020-11-23
126
Neural Networks Pt. 4: Multiple Inputs and Outputs
13:50
2021-02-01
127
StatQuest: How to make a Mean Pizza Crust!!!
8:12
2017-08-31
128
Private video
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129
Private video
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130
Private video
—
131
Private video
—
132
US Census Data and Contest!!!
8:36
2020-12-08
133
Neural Networks Part 5: ArgMax and SoftMax
14:03
2021-02-08
134
The SoftMax Derivative, Step-by-Step!!!
7:13
2021-02-08
135
Neural Networks Part 6: Cross Entropy
9:31
2021-03-01
136
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
22:08
2021-03-01
137
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
15:24
2021-03-08
138
Silly Songs, Clearly Explained!!!
3:53
2021-04-01
139
Decision and Classification Trees, Clearly Explained!!!
18:08
2021-04-26
140
How to make your own StatQuest!!!
5:00
2021-05-03
141
Three (3) things to do when starting out in Data Science
11:10
2021-05-27
142
Ken Jee's #66DaysOfData Challenge Clearly Explained!!!
6:22
2021-06-23
143
Bootstrapping Main Ideas!!!
9:27
2021-07-06
144
Using Bootstrapping to Calculate p-values!!!
8:08
2021-07-13
145
Conditional Probabilities, Clearly Explained!!!
10:56
2021-07-14
146
Conditional Probabilities, Clearly Explained!!!
10:56
2021-07-21
147
Bayes' Theorem, Clearly Explained!!!!
14:00
2021-08-16
148
Entropy (for data science) Clearly Explained!!!
16:35
2021-08-25
149
Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)
6:28
2021-09-04
150
p-values: What they are and how to interpret them
11:21
2021-12-03
151
Clustering with DBSCAN, Clearly Explained!!!
9:30
2022-01-10
152
Tensors for Neural Networks, Clearly Explained!!!
9:40
2022-02-28
153
Troll 2, Clearly Explained!!!
5:06
2022-04-01
154
Recurrent Neural Networks (RNNs), Clearly Explained!!!
16:37
2022-07-11
155
Long Short-Term Memory (LSTM), Clearly Explained
20:45
2022-11-07
156
The StatQuest Introduction to PyTorch
23:22
2022-04-25
157
Introduction to Coding Neural Networks with PyTorch and Lightning
20:43
2022-09-19
158
Cosine Similarity, Clearly Explained!!!
10:14
2023-01-30
159
Mutual Information, Clearly Explained!!!
16:14
2023-02-06
160
One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
15:23
2023-02-13
161
Word Embedding and Word2Vec, Clearly Explained!!!
16:12
2023-03-13
162
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
16:50
2023-05-08
163
Attention for Neural Networks, Clearly Explained!!!
15:51
2023-06-05
164
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
36:15
2023-07-24
165
Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
36:45
2023-08-28
166
Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
18:52
2024-11-18
167
The Golden Play Button, Clearly Explained!!!’
2:30
2023-10-07
168
Word Embedding in PyTorch + Lightning
32:02
2023-11-06
169
Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
30:01
2023-12-11
170
The matrix math behind transformer neural networks, one step at a time!!!
23:43
2024-04-08
171
Coding a ChatGPT Like Transformer From Scratch in PyTorch
31:11
2024-07-01
172
Reinforcement Learning: Essential Concepts
18:13
2025-03-31
173
Reinforcement Learning with Neural Networks: Essential Concepts
24:00
2025-04-07
174
Reinforcement Learning with Neural Networks: Mathematical Details
25:01
2025-04-14
175
Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
18:02
2025-05-05
176
CatBoost Part 1: Ordered Target Encoding
8:32
2023-02-27
177
CatBoost Part 2: Building and Using Trees
16:16
2023-03-06
178
The Essence of Linear Regression!!!
32:01
2026-05-18
