Playlist Duration

Playlist · StatQuest with Josh Starmer

Machine Learning

by StatQuest with Josh Starmer · 106 videos

Listed in Best free machine learning courses on YouTube, ranked by total hours

Total watch time

1d 5h 51m

at 1× speed · exactly 1 day, 5 hours, 50 minutes, 35 seconds at 1×

At an hour a day you'd finish in 30 days

The shape of the course

106 videos in blocks of 10 · each column is a block's total, to scale

1h 55m
2h 12m
2h 18m
1h 32m
2h 6m
3h 39m
4h 4m
2h 56m
2h 33m
4h 9m
2h 28m

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Videos (106)

1d 5h 51m in total · tick what you've watched

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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
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