1
StatQuest: Histograms, Clearly Explained
3:42
2017-09-25
2
The Main Ideas behind Probability Distributions
5:15
2017-04-17
3
The Normal Distribution, Clearly Explained!!!
5:13
2017-10-09
4
Population and Estimated Parameters, Clearly Explained!!!
14:31
2019-07-01
5
Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!
14:22
2019-07-15
6
What is a (mathematical) model?
3:45
2017-07-17
7
Sampling from a Distribution, Clearly Explained!!!
3:49
2017-05-08
8
Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!
14:41
2020-07-06
9
p-values: What they are and how to interpret them
11:21
2020-03-23
10
How to calculate p-values
25:15
2020-03-23
11
p-hacking: What it is and how to avoid it!
13:45
2020-05-04
12
False Discovery Rates, FDR, clearly explained
18:27
2026-06-08
13
Statistical Power, Clearly Explained!!!
8:19
2020-05-04
14
Power Analysis, Clearly Explained!!!
16:45
2020-05-04
15
The Central Limit Theorem, Clearly Explained!!!
7:35
2018-09-03
16
Standard Deviation vs Standard Error, Clearly Explained!!!
2:52
2017-03-20
17
Logs (logarithms), Clearly Explained!!!
15:37
2017-02-23
18
Linear Regression, Clearly Explained!!!
27:27
2022-11-18
19
Multiple Regression, Clearly Explained!!!
5:25
2017-10-30
20
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
11:38
2017-08-07
21
Design Matrices For Linear Models, Clearly Explained!!!
14:40
2019-01-08
22
In Statistics, Probability is not Likelihood.
5:01
2018-03-09
23
Maximum Likelihood, clearly explained!!!
6:12
2017-07-31
24
Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!
9:39
2018-07-30
25
Odds and Log(Odds), Clearly Explained!!!
11:31
2018-05-07
26
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
16:20
2018-06-21
27
A Gentle Introduction to Machine Learning
12:45
2018-11-26
28
Machine Learning Fundamentals: Cross Validation
6:05
2018-04-24
29
Machine Learning Fundamentals: The Confusion Matrix
7:13
2018-10-29
30
Machine Learning Fundamentals: Sensitivity and Specificity
11:47
2019-12-02
31
Machine Learning Fundamentals: Bias and Variance
6:36
2018-09-17
32
ROC and AUC, Clearly Explained!
16:17
2019-07-11
33
Regularization Part 1: Ridge (L2) Regression
20:27
2018-09-24
34
Regularization Part 2: Lasso (L1) Regression
8:19
2018-10-01
35
Ridge vs Lasso Regression, Visualized!!!
9:06
2020-05-19
36
Regularization Part 3: Elastic Net Regression
5:19
2018-10-08
37
StatQuest: Principal Component Analysis (PCA), Step-by-Step
21:58
2018-04-02
38
StatQuest: PCA - Practical Tips
8:20
2018-04-09
39
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
15:12
2016-07-10
40
StatQuest: MDS and PCoA
8:18
2017-12-11
41
StatQuest: t-SNE, Clearly Explained
11:48
2017-09-18
42
StatQuest: Hierarchical Clustering
11:19
2017-06-20
43
StatQuest: K-means clustering
8:30
2018-05-23
44
StatQuest: K-nearest neighbors, Clearly Explained
5:30
2017-06-26
45
Naive Bayes, Clearly Explained!!!
15:12
2020-06-03
46
Gaussian Naive Bayes, Clearly Explained!!!
9:26
2020-06-03
47
The Chain Rule, Clearly Explained!!!
18:24
2020-07-13
48
Gradient Descent, Step-by-Step
23:54
2019-02-05
49
Stochastic Gradient Descent, Clearly Explained!!!
10:53
2019-05-13
50
Decision and Classification Trees, Clearly Explained!!!
18:08
2021-04-26
51
Regression Trees, Clearly Explained!!!
22:33
2019-08-20
52
StatQuest: Random Forests Part 1 - Building, Using and Evaluating
9:54
2018-02-05
53
AdaBoost, Clearly Explained
20:54
2019-01-14
54
Three (3) things to do when starting out in Data Science
11:10
2021-05-27
55
Gradient Boost Part 1 (of 4): Regression Main Ideas
15:52
2019-03-25
56
Gradient Boost Part 3 (of 4): Classification
17:03
2019-04-08
57
XGBoost Part 1 (of 4): Regression
25:46
2019-12-16
58
XGBoost Part 2 (of 4): Classification
25:18
2020-01-13
59
Support Vector Machines Part 1 (of 3): Main Ideas!!!
20:32
2019-09-30
60
StatQuest: Logistic Regression
8:48
2018-03-05
61
Logistic Regression Details Pt1: Coefficients
19:02
2018-06-04
62
The Essential Main Ideas of Neural Networks
18:54
2020-08-31
63
Neural Networks Pt. 2: Backpropagation Main Ideas
17:34
2020-10-19
64
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
18:32
2020-11-02
65
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
13:09
2020-11-02
66
Neural Networks Pt. 3: ReLU In Action!!!
8:58
2020-11-23
67
Neural Networks Pt. 4: Multiple Inputs and Outputs
13:50
2021-02-01
68
Neural Networks Part 5: ArgMax and SoftMax
14:03
2021-02-08
69
The SoftMax Derivative, Step-by-Step!!!
7:13
2021-02-08
70
Neural Networks Part 6: Cross Entropy
9:31
2021-03-01
71
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
22:08
2021-03-01
72
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
15:24
2021-03-08
73
Ken Jee's #66DaysOfData Challenge Clearly Explained!!!
6:22
2021-06-23
74
p-values: What they are and how to interpret them
11:21
2021-12-03
