1
A Gentle Introduction to Machine Learning
12:45
2018-11-26
2
Machine Learning Fundamentals: Cross Validation
6:05
2018-04-24
3
Machine Learning Fundamentals: The Confusion Matrix
7:13
2018-10-29
4
Machine Learning Fundamentals: Sensitivity and Specificity
11:47
2019-12-02
5
The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!
0:42
2022-02-10
6
Machine Learning Fundamentals: Bias and Variance
6:36
2018-09-17
7
Entropy (for data science) Clearly Explained!!!
16:35
2021-08-25
8
Mutual Information, Clearly Explained!!!
16:14
2023-02-06
9
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
9:22
2017-05-22
10
Linear Regression, Clearly Explained!!!
27:27
2022-11-18
11
Multiple Regression, Clearly Explained!!!
5:25
2017-10-30
12
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
11:38
2017-08-07
13
Design Matrices For Linear Models, Clearly Explained!!!
14:40
2019-01-08
14
Odds and Log(Odds), Clearly Explained!!!
11:31
2018-05-07
15
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
16:20
2018-06-21
16
StatQuest: Logistic Regression
8:48
2018-03-05
17
Logistic Regression Details Pt1: Coefficients
19:02
2018-06-04
18
Logistic Regression Details Pt 2: Maximum Likelihood
10:23
2018-06-11
19
Logistic Regression Details Pt 3: R-squared and p-value
15:25
2018-06-18
20
Saturated Models and Deviance
18:40
2018-07-09
21
Logistic Regression in R, Clearly Explained!!!!
17:15
2018-07-26
22
Deviance Residuals
6:18
2018-07-16
23
ROC and AUC, Clearly Explained!
16:17
2019-07-11
24
ROC and AUC in R
15:13
2018-12-18
25
Regularization Part 1: Ridge (L2) Regression
20:27
2018-09-24
26
Regularization Part 2: Lasso (L1) Regression
8:19
2018-10-01
27
Ridge vs Lasso Regression, Visualized!!!
9:06
2020-05-19
28
Regularization Part 3: Elastic Net Regression
5:19
2018-10-08
29
Ridge, Lasso and Elastic-Net Regression in R
17:51
2018-10-23
30
StatQuest: Principal Component Analysis (PCA), Step-by-Step
21:58
2018-04-02
31
StatQuest: PCA main ideas in only 5 minutes!!!
6:05
2017-12-04
32
StatQuest: PCA - Practical Tips
8:20
2018-04-09
33
StatQuest: PCA in R
8:57
2017-11-27
34
StatQuest: PCA in Python
11:37
2018-01-08
35
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
15:12
2016-07-10
36
Bam!!! Clearly Explained!!!
2:49
2020-04-01
37
StatQuest: MDS and PCoA
8:18
2017-12-11
38
StatQuest: MDS and PCoA in R
7:45
2017-12-18
39
StatQuest: t-SNE, Clearly Explained
11:48
2017-09-18
40
StatQuest: Hierarchical Clustering
11:19
2017-06-20
41
StatQuest: K-means clustering
8:30
2018-05-23
42
Clustering with DBSCAN, Clearly Explained!!!
9:30
2022-01-10
43
StatQuest: K-nearest neighbors, Clearly Explained
5:30
2017-06-26
44
Naive Bayes, Clearly Explained!!!
15:12
2020-06-03
45
Gaussian Naive Bayes, Clearly Explained!!!
9:26
2020-06-03
46
Decision and Classification Trees, Clearly Explained!!!
18:08
2021-04-26
47
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
5:16
2018-01-29
48
Regression Trees, Clearly Explained!!!
22:33
2019-08-20
49
How to Prune Regression Trees, Clearly Explained!!!
16:15
2019-11-25
50
One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
15:23
2023-02-13
51
Classification Trees in Python from Start to Finish
1:06:24
2020-06-07
52
StatQuest: Random Forests Part 1 - Building, Using and Evaluating
9:54
2018-02-05
53
StatQuest: Random Forests Part 2: Missing data and clustering
10:48
2026-06-08
54
StatQuest: Random Forests in R
15:10
2018-02-26
55
The Chain Rule, Clearly Explained!!!
18:24
2020-07-13
56
Gradient Descent, Step-by-Step
23:54
2019-02-05
57
Stochastic Gradient Descent, Clearly Explained!!!
10:53
2019-05-13
58
AdaBoost, Clearly Explained
20:54
2019-01-14
59
Gradient Boost Part 1 (of 4): Regression Main Ideas
15:52
2019-03-25
60
Gradient Boost Part 2 (of 4): Regression Details
26:46
2019-04-01
61
Gradient Boost Part 3 (of 4): Classification
17:03
2019-04-08
62
Gradient Boost Part 4 (of 4): Classification Details
37:00
2019-04-22
63
Troll 2, Clearly Explained!!!
5:06
2022-04-01
64
XGBoost Part 1 (of 4): Regression
25:46
2019-12-16
65
XGBoost Part 2 (of 4): Classification
25:18
2020-01-13
66
XGBoost Part 3 (of 4): Mathematical Details
27:24
2020-02-10
67
XGBoost Part 4 (of 4): Crazy Cool Optimizations
24:27
2020-03-02
68
XGBoost in Python from Start to Finish
56:43
2020-08-01
69
CatBoost Part 1: Ordered Target Encoding
8:32
2023-02-27
70
CatBoost Part 2: Building and Using Trees
16:16
2023-03-06
71
Cosine Similarity, Clearly Explained!!!
10:14
2023-01-30
72
Support Vector Machines Part 1 (of 3): Main Ideas!!!
20:32
2019-09-30
73
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
7:15
2019-11-04
74
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
15:52
2019-11-04
75
Support Vector Machines in Python from Start to Finish.
44:49
2020-06-30
76
The Essential Main Ideas of Neural Networks
18:54
2020-08-31
77
Neural Networks Pt. 2: Backpropagation Main Ideas
17:34
2020-10-19
78
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
18:32
2020-11-02
79
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
13:09
2020-11-02
80
Neural Networks Pt. 3: ReLU In Action!!!
8:58
2020-11-23
81
Neural Networks Pt. 4: Multiple Inputs and Outputs
13:50
2021-02-01
82
Neural Networks Part 5: ArgMax and SoftMax
14:03
2021-02-08
83
The SoftMax Derivative, Step-by-Step!!!
7:13
2021-02-08
84
Neural Networks Part 6: Cross Entropy
9:31
2021-03-01
85
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
22:08
2021-03-01
86
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
15:24
2021-03-08
87
Recurrent Neural Networks (RNNs), Clearly Explained!!!
16:37
2022-07-11
88
Long Short-Term Memory (LSTM), Clearly Explained
20:45
2022-11-07
89
Word Embedding and Word2Vec, Clearly Explained!!!
16:12
2023-03-13
90
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
16:50
2023-05-08
91
Attention for Neural Networks, Clearly Explained!!!
15:51
2023-06-05
92
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
36:15
2023-07-24
93
Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
36:45
2023-08-28
94
Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
18:52
2024-11-18
95
Tensors for Neural Networks, Clearly Explained!!!
9:40
2022-02-28
96
Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
30:01
2023-12-11
97
The matrix math behind transformer neural networks, one step at a time!!!
23:43
2024-04-08
98
The StatQuest Introduction to PyTorch
23:22
2022-04-25
99
Introduction to Coding Neural Networks with PyTorch and Lightning
20:43
2022-09-19
100
Long Short-Term Memory with PyTorch + Lightning
33:24
2023-01-24
101
Word Embedding in PyTorch + Lightning
32:02
2023-11-06
102
Coding a ChatGPT Like Transformer From Scratch in PyTorch
31:11
2024-07-01
103
Reinforcement Learning: Essential Concepts
18:13
2025-03-31
104
Reinforcement Learning with Neural Networks: Essential Concepts
24:00
2025-04-07
105
Reinforcement Learning with Neural Networks: Mathematical Details
25:01
2025-04-14
106
Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
18:02
2025-05-05
