Machine Learning covers a lot of topics and this can be intimidating. However, there is no reason to fear, this play list will help you trough it all, one step at a time.
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A Gentle Introduction to Machine Learning
Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut …
1
Machine Learning Fundamentals: Cross Validation
One of the fundamental concepts in machine learning is Cross Validation. It's how we decide which machine learning metho…
2
Machine Learning Fundamentals: The Confusion Matrix
One of the fundamental concepts in machine learning is the Confusion Matrix. Combined with Cross Validation, it's how we…
3
Machine Learning Fundamentals: Sensitivity and Specificity
In this StatQuest we talk about Sensitivity and Specificity - to key concepts for evaluating Machine Learning methods. T…
4
The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!
I always had trouble remembering exactly what sensitivity, specificity, precision and recall all meant, so I wrote this …
5
Machine Learning Fundamentals: Bias and Variance
Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from…
6
Entropy (for data science) Clearly Explained!!!
Entropy is a fundamental concept in Data Science because it shows up all over the place - from Decision Trees, to simila…
7
Mutual Information, Clearly Explained!!!
Mutual Information is metric that quantifies how similar or different two variables are. This is a lot like R-squared, b…
8
The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
Fitting a line to data is actually pretty straightforward.
For a complete index of all the StatQuest videos, check out:…
9
Linear Regression, Clearly Explained!!!
The concepts behind linear regression, fitting a line to data with least squares and R-squared, are pretty darn simple, …
10
Multiple Regression, Clearly Explained!!!
This video directly follows part 1 in the StatQuest series on General Linear Models (GLMs) on Linear Regression https://…
11
Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
This StatQuest shows how the methods used to determine if a linear regression is statistically significant (covered in p…
12
Design Matrices For Linear Models, Clearly Explained!!!
In order to use general linear models (GLMs) you need to create design matrices. At first, these can seem intimidating, …
13
Odds and Log(Odds), Clearly Explained!!!
The odds aren't as odd as you might think, and the log of the odds is even simpler! This StatQuest covers those subjects…
14
Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
Odds Ratios and Log(Odds Ratios) are like R-Squared - they describe a relationship between two things. And just like R-S…
15
StatQuest: Logistic Regression
Logistic regression is a traditional statistics technique that is also very popular as a machine learning tool. In this …
16
Logistic Regression Details Pt1: Coefficients
When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odd…
17
Logistic Regression Details Pt 2: Maximum Likelihood
This video follows from where we left off in Part 1 in this series on the details of Logistic Regression. This time we'r…
18
Logistic Regression Details Pt 3: R-squared and p-value
This video follows from where we left off in Part 2 in this series on the details of Logistic Regression. Last time we …
19
Saturated Models and Deviance
This video follows from where we left off in Part 3 of the Logistic Regression series, but the ideas are more general, s…
20
Logistic Regression in R, Clearly Explained!!!!
This video describes how to do Logistic Regression in R, step-by-step. We start by importing a dataset and cleaning it u…
21
Deviance Residuals
This video follows up on the StatQuest on Saturated Models and Deviance Statistics. https://youtu.be/9T0wlKdew6I
For a …
22
ROC and AUC, Clearly Explained!
ROC (Receiver Operator Characteristic) graphs and AUC (the area under the curve), are useful for consolidating the infor…
23
ROC and AUC in R
This tutorial walks you through, step-by-step, how to draw ROC curves and calculate AUC in R. We start with basic ROC gr…
24
Regularization Part 1: Ridge (L2) Regression
Ridge Regression is a neat little way to ensure you don't overfit your training data - essentially, you are desensitizin…
25
Regularization Part 2: Lasso (L1) Regression
Lasso Regression is super similar to Ridge Regression, but there is one big, huge difference between the two. In this vi…
26
Ridge vs Lasso Regression, Visualized!!!
People often ask why Lasso Regression can make parameter values equal 0, but Ridge Regression can not. This StatQuest sh…
27
Regularization Part 3: Elastic Net Regression
Elastic-Net Regression is combines Lasso Regression with Ridge Regression to give you the best of both worlds. It works …
28
Ridge, Lasso and Elastic-Net Regression in R
The code in this video can be found on the StatQuest GitHub:
https://github.com/StatQuest/ridge_lasso_elastic_net_demo/b…
29
StatQuest: Principal Component Analysis (PCA), Step-by-Step
Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be …
30
StatQuest: PCA main ideas in only 5 minutes!!!
The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are c…
31
StatQuest: PCA - Practical Tips
This is a follow-up video for StatQuest: Principal Component Analysis (PCA), Step-by-Step https://youtu.be/FgakZw6K1QQ
…
32
StatQuest: PCA in R
We've talked about the theory behind PCA in
https://youtu.be/FgakZw6K1QQ
Now we talk about how to do it in practice usi…
33
StatQuest: PCA in Python
You asked for it, you got it! Now I walk you through how to do PCA in Python, step-by-step. It's not too bad, and I'll s…
34
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations t…
35
Bam!!! Clearly Explained!!!
This StatQuests demystifies one of the most complicated terms in all of statistics and machine learning, BAM!!! We'll wa…
36
StatQuest: MDS and PCoA
MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) are very, very similar to PCA (principal compon…
37
StatQuest: MDS and PCoA in R
Doing MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) is pretty easy with the cmdscale() option…
38
StatQuest: t-SNE, Clearly Explained
t-SNE is a popular method for making an easy to read graph from a complex dataset, but not many people know how it works…
39
StatQuest: Hierarchical Clustering
Hierarchical clustering is often used with heatmaps and with machine learning type stuff. It's no big deal, though, and …
40
StatQuest: K-means clustering
K-means clustering is used in all kinds of situations and it's crazy simple. The R code is on the StatQuest GitHub: http…
41
Clustering with DBSCAN, Clearly Explained!!!
DBSCAN is a super useful clustering algorithm that can handle nested clusters with ease. This StatQuest shows you exactl…
42
StatQuest: K-nearest neighbors, Clearly Explained
Machine learning and Data Mining sure sound like complicated things, but that isn't always the case. Here we talk about …
43
Naive Bayes, Clearly Explained!!!
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - whic…
44
Gaussian Naive Bayes, Clearly Explained!!!
Gaussian Naive Bayes takes are of all your Naive Bayes needs when your training data are continuous. If that sounds fanc…
45
Decision and Classification Trees, Clearly Explained!!!
Decision trees are part of the foundation for Machine Learning. Although they are quite simple, they are very flexible a…
46
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
This is just a short follow up to last week's StatQuest where we introduced decision trees. Here we show how decision tr…
47
Regression Trees, Clearly Explained!!!
Regression Trees are one of the fundamental machine learning techniques that more complicated methods, like Gradient Boo…
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How to Prune Regression Trees, Clearly Explained!!!
Pruning Regression Trees is one the most important ways we can prevent them from overfitting the Training Data. This vid…
49
One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
In theory, discrete variables, or features, are easy to use with machine learning algorithms. However, in practice, it's…
50
Classification Trees in Python from Start to Finish
NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: https://stat…
51
StatQuest: Random Forests Part 1 - Building, Using and Evaluating
Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don't hav…
52
StatQuest: Random Forests Part 2: Missing data and clustering
NOTE: This StatQuest is the updated version of the original Random Forests Part 3 and includes two minor corrections.
L…
53
StatQuest: Random Forests in R
Random Forests are an easy to understand and easy to use machine learning technique that is surprisingly powerful. Here …
54
The Chain Rule, Clearly Explained!!!
The Chain Rule is a method for finding complex derivatives and is used all the time in Statistics and Machine Learning. …
55
Gradient Descent, Step-by-Step
Gradient Descent is the workhorse behind most of Machine Learning. When you fit a machine learning method to a training …
56
Stochastic Gradient Descent, Clearly Explained!!!
Even though Stochastic Gradient Descent sounds fancy, it is just a simple addition to "regular" Gradient Descent. This v…
57
AdaBoost, Clearly Explained
AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just …
58
Gradient Boost Part 1 (of 4): Regression Main Ideas
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! T…
59
Gradient Boost Part 2 (of 4): Regression Details
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! T…
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Gradient Boost Part 3 (of 4): Classification
This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This…
61
Gradient Boost Part 4 (of 4): Classification Details
At last, part 4 in our series of videos on Gradient Boost. This time we dive deep into the details of how it is used for…
62
Troll 2, Clearly Explained!!!
This year's April Fools' (April 1st) StatQuest demystifies one of the most poorly understood datasets in StatQuest video…
63
XGBoost Part 1 (of 4): Regression
XGBoost is an extreme machine learning algorithm, and that means it's got lots of parts. In this video, we focus on the …
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XGBoost Part 2 (of 4): Classification
In this video we pick up where we left off in part 1 and cover how XGBoost trees are built for Classification.
NOTE: Th…
65
XGBoost Part 3 (of 4): Mathematical Details
In this video we dive into the nitty-gritty details of the math behind XGBoost trees. We derive the equations for the O…
66
XGBoost Part 4 (of 4): Crazy Cool Optimizations
This video covers all kinds of extra optimizations that XGBoost uses when the training dataset is huge. So we'll talk ab…
67
XGBoost in Python from Start to Finish
NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: https://stat…
68
CatBoost Part 1: Ordered Target Encoding
One of the defining features of CatBoost is its concerted effort to avoid data leakage at all costs. In this video, we'l…
69
CatBoost Part 2: Building and Using Trees
Just like we saw in CatBoost Part 1, Ordered Target Encoding, we're going to use the training data one row at a time to …
70
Cosine Similarity, Clearly Explained!!!
The Cosine Similarity is a useful metric for determining, among other things, how similar or different two text phrases …
71
Support Vector Machines Part 1 (of 3): Main Ideas!!!
Support Vector Machines are one of the most mysterious methods in Machine Learning. This StatQuest sweeps away the myste…
72
Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
Support Vector Machines use kernel functions to do all the hard work and this StatQuest dives deep into one of the most …
73
Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
Support Vector Machines use kernel functions to do all the hard work and this StatQuest dives deep into one of the most …
74
Support Vector Machines in Python from Start to Finish.
NOTE: You can support StatQuest by purchasing the Jupyter Notebook and Python code seen in this video here: http://statq…
75
The Essential Main Ideas of Neural Networks
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly unders…
76
Neural Networks Pt. 2: Backpropagation Main Ideas
Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are qu…
77
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
The main ideas behind Backpropagation are super simple, but there are tons of details when it comes time to implementing…
78
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
This StatQuest picks up right here Part 1 left off, and this time we're going to go totally bonkers with The Chain Rule …
79
Neural Networks Pt. 3: ReLU In Action!!!
The ReLU activation function is one of the most popular activation functions for Deep Learning and Convolutional Neural …
80
Neural Networks Pt. 4: Multiple Inputs and Outputs
So far, this series has explained how very simple Neural Networks, with only 1 input and 1 output, function. This video …
81
Neural Networks Part 5: ArgMax and SoftMax
When your Neural Network has more than one output, then it is very common to train with SoftMax and, once trained, swap …
82
The SoftMax Derivative, Step-by-Step!!!
Here's step-by-step guide that shows you how to take the derivatives of the SoftMax function, as used as a final output …
83
Neural Networks Part 6: Cross Entropy
When a Neural Network is used for classification, we usually evaluate how well it fits the data with Cross Entropy. This…
84
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
Here is a step-by-step guide that shows you how to take the derivative of the Cross Entropy function for Neural Networks…
85
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
One of the coolest things that Neural Networks can do is classify images, and this is often done with a type of Neural N…
86
Recurrent Neural Networks (RNNs), Clearly Explained!!!
When you don't always have the same amount of data, like when translating different sentences from one language to anoth…
87
Long Short-Term Memory (LSTM), Clearly Explained
Basic recurrent neural networks are great, because they can handle different amounts of sequential data, but even relati…
88
Word Embedding and Word2Vec, Clearly Explained!!!
Words are great, but if we want to use them as input to a neural network, we have to convert them to numbers. One of the…
89
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
In this video, we introduce the basics of how Neural Networks translate one language, like English, to another, like Spa…
90
Attention for Neural Networks, Clearly Explained!!!
Attention is one of the most important concepts behind Transformers and Large Language Models, like ChatGPT. However, it…
91
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate …
92
Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
Transformers are taking over AI right now, and quite possibly their most famous use is in ChatGPT. ChatGPT uses a specif…
93
Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
Encoder-Only Transformers are the backbone for RAG (retrieval augmented generation), sentiment analysis and classificati…
94
Tensors for Neural Networks, Clearly Explained!!!
Tensors are super important for neural networks, but can be confusing because different people use the word "Tensor" dif…
95
Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
Although you don't need to know matrix algebra to understand the ideas behind neural networks, if you want to code them …
96
The matrix math behind transformer neural networks, one step at a time!!!
Transformers, the neural network architecture behind ChatGPT, do a lot of math. However, this math can be done quickly u…
97
The StatQuest Introduction to PyTorch
PyTorch is one of the most popular tools for making Neural Networks. This StatQuest walks you through a simple example o…
98
Introduction to Coding Neural Networks with PyTorch and Lightning
Although we've seen how to code a simple neural network with PyTorch, we can make our lives a lot easier if we add Light…
99
Long Short-Term Memory with PyTorch + Lightning
In this StatQuest we'll learn how to code an LSTM unit from scratch and then train it. Then we'll do the same thing with…
100
Word Embedding in PyTorch + Lightning
Word embedding is the first step in lots of neural networks, including Transformers (like ChatGPT) and other state of th…
101
Coding a ChatGPT Like Transformer From Scratch in PyTorch
In this StatQuest we walk through the code required to code your own ChatGPT like Transformer in PyTorch and we do it on…
102
Reinforcement Learning: Essential Concepts
Reinforcement Learning is one of the most useful methodologies for training AI systems right now, and, while it might se…
103
Reinforcement Learning with Neural Networks: Essential Concepts
Reinforcement Learning has helped train neural networks to win games, drive cars and even get ChatGPT to sound more huma…
104
Reinforcement Learning with Neural Networks: Mathematical Details
Here we go through the math required to update a parameter in a neural network using reinforcement learning and we do it…
105
Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
Generative Large Language Models, like ChatGPT and DeepSeek, are trained on massive text based datasets, like the entire…
