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StatQuest Machine Learning: study plan and how long it takes

The StatQuest with Josh Starmer playlist is 106 videos and 29.8 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.

106 videos · 29.8 hours · StatQuest with Josh Starmer

Josh Starmer's StatQuest Machine Learning playlist explains ML concepts "clearly", one at a time: regression and regularization, logistic regression, PCA, clustering, decision trees, random forests, gradient boosting and XGBoost, support vector machines, neural networks, attention, word embeddings and RLHF.

Each video takes a single idea and makes it intuitive, with Josh's trademark songs and "BAM!" along the way.

Who it's for: Students and practitioners who want intuitive explanations of the algorithms behind machine learning.

How long does it take?

The full playlist is 29.8 hours across 106 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:

Days to finish the playlist by daily watch time and playback speed
Daily timeAt 1xAt 1.5xAt 2x
30 min a day60 days40 days30 days
1 h a day30 days20 days15 days
2 h a day15 days10 days8 days

Make your own schedule

The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.

Open on YouTube Track watched videos

Week-by-week schedule

A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 6 weeks in total. For a different pace, start date or speed, use the planner above.

Week 1Videos 1–28 · 5h 45m

  1. Day 1 · 45m

    • 1A Gentle Introduction to Machine Learning13m
    • 2Machine Learning Fundamentals: Cross Validation6m
    • 3Machine Learning Fundamentals: The Confusion Matrix7m
    • 4Machine Learning Fundamentals: Sensitivity and Specificity12m
    • 5The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1m
    • 6Machine Learning Fundamentals: Bias and Variance7m

  2. Day 2 · 42m

    • 7Entropy (for data science) Clearly Explained!!!17m
    • 8Mutual Information, Clearly Explained!!!16m
    • 9The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9m

  3. Day 3 · 59m

    • 10Linear Regression, Clearly Explained!!!27m
    • 11Multiple Regression, Clearly Explained!!!5m
    • 12Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12m
    • 13Design Matrices For Linear Models, Clearly Explained!!!15m

  4. Day 4 · 56m

    • 14Odds and Log(Odds), Clearly Explained!!!12m
    • 15Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16m
    • 16StatQuest: Logistic Regression9m
    • 17Logistic Regression Details Pt1: Coefficients19m

  5. Day 5 · 44m

    • 18Logistic Regression Details Pt 2: Maximum Likelihood10m
    • 19Logistic Regression Details Pt 3: R-squared and p-value15m
    • 20Saturated Models and Deviance19m

  6. Day 6 · 55m

    • 21Logistic Regression in R, Clearly Explained!!!!17m
    • 22Deviance Residuals6m
    • 23ROC and AUC, Clearly Explained!16m
    • 24ROC and AUC in R15m

  7. Day 7 · 43m

    • 25Regularization Part 1: Ridge (L2) Regression20m
    • 26Regularization Part 2: Lasso (L1) Regression8m
    • 27Ridge vs Lasso Regression, Visualized!!!9m
    • 28Regularization Part 3: Elastic Net Regression5m
Week 2Videos 29–55 · 6h 18m

  1. Day 8 · 54m

    • 29Ridge, Lasso and Elastic-Net Regression in R18m
    • 30StatQuest: Principal Component Analysis (PCA), Step-by-Step22m
    • 31StatQuest: PCA main ideas in only 5 minutes!!!6m
    • 32StatQuest: PCA - Practical Tips8m

  2. Day 9 · 55m

    • 33StatQuest: PCA in R9m
    • 34StatQuest: PCA in Python12m
    • 35StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15m
    • 36Bam!!! Clearly Explained!!!3m
    • 37StatQuest: MDS and PCoA8m
    • 38StatQuest: MDS and PCoA in R8m

  3. Day 10 · 47m

    • 39StatQuest: t-SNE, Clearly Explained12m
    • 40StatQuest: Hierarchical Clustering11m
    • 41StatQuest: K-means clustering9m
    • 42Clustering with DBSCAN, Clearly Explained!!!10m
    • 43StatQuest: K-nearest neighbors, Clearly Explained6m

  4. Day 11 · 48m

    • 44Naive Bayes, Clearly Explained!!!15m
    • 45Gaussian Naive Bayes, Clearly Explained!!!9m
    • 46Decision and Classification Trees, Clearly Explained!!!18m
    • 47StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5m

  5. Day 12 · 54m

    • 48Regression Trees, Clearly Explained!!!23m
    • 49How to Prune Regression Trees, Clearly Explained!!!16m
    • 50One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15m

  6. Day 13 · 1h 6m

    • 51Classification Trees in Python from Start to Finish1h 6m

  7. Day 14 · 54m

    • 52StatQuest: Random Forests Part 1 - Building, Using and Evaluating10m
    • 53StatQuest: Random Forests Part 2: Missing data and clustering11m
    • 54StatQuest: Random Forests in R15m
    • 55The Chain Rule, Clearly Explained!!!18m
Week 3Videos 56–72 · 6h 13m

  1. Day 15 · 56m

    • 56Gradient Descent, Step-by-Step24m
    • 57Stochastic Gradient Descent, Clearly Explained!!!11m
    • 58AdaBoost, Clearly Explained21m

  2. Day 16 · 1h

    • 59Gradient Boost Part 1 (of 4): Regression Main Ideas16m
    • 60Gradient Boost Part 2 (of 4): Regression Details27m
    • 61Gradient Boost Part 3 (of 4): Classification17m

  3. Day 17 · 42m

    • 62Gradient Boost Part 4 (of 4): Classification Details37m
    • 63Troll 2, Clearly Explained!!!5m

  4. Day 18 · 51m

    • 64XGBoost Part 1 (of 4): Regression26m
    • 65XGBoost Part 2 (of 4): Classification25m

  5. Day 19 · 52m

    • 66XGBoost Part 3 (of 4): Mathematical Details27m
    • 67XGBoost Part 4 (of 4): Crazy Cool Optimizations24m

  6. Day 20 · 57m

    • 68XGBoost in Python from Start to Finish57m

  7. Day 21 · 56m

    • 69CatBoost Part 1: Ordered Target Encoding9m
    • 70CatBoost Part 2: Building and Using Trees16m
    • 71Cosine Similarity, Clearly Explained!!!10m
    • 72Support Vector Machines Part 1 (of 3): Main Ideas!!!21m
Week 4Videos 73–91 · 5h 13m

  1. Day 22 · 23m

    • 73Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7m
    • 74Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16m

  2. Day 23 · 45m

    • 75Support Vector Machines in Python from Start to Finish.45m

  3. Day 24 · 55m

    • 76The Essential Main Ideas of Neural Networks19m
    • 77Neural Networks Pt. 2: Backpropagation Main Ideas18m
    • 78Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.19m

  4. Day 25 · 57m

    • 79Backpropagation Details Pt. 2: Going bonkers with The Chain Rule13m
    • 80Neural Networks Pt. 3: ReLU In Action!!!9m
    • 81Neural Networks Pt. 4: Multiple Inputs and Outputs14m
    • 82Neural Networks Part 5: ArgMax and SoftMax14m
    • 83The SoftMax Derivative, Step-by-Step!!!7m

  5. Day 26 · 47m

    • 84Neural Networks Part 6: Cross Entropy10m
    • 85Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation22m
    • 86Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)15m

  6. Day 27 · 54m

    • 87Recurrent Neural Networks (RNNs), Clearly Explained!!!17m
    • 88Long Short-Term Memory (LSTM), Clearly Explained21m
    • 89Word Embedding and Word2Vec, Clearly Explained!!!16m

  7. Day 28 · 33m

    • 90Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!17m
    • 91Attention for Neural Networks, Clearly Explained!!!16m
Week 5Videos 92–103 · 5h 14m

  1. Day 29 · 36m

    • 92Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!36m

  2. Day 30 · 56m

    • 93Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!37m
    • 94Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!19m

  3. Day 31 · 40m

    • 95Tensors for Neural Networks, Clearly Explained!!!10m
    • 96Essential Matrix Algebra for Neural Networks, Clearly Explained!!!30m

  4. Day 32 · 47m

    • 97The matrix math behind transformer neural networks, one step at a time!!!24m
    • 98The StatQuest Introduction to PyTorch23m

  5. Day 33 · 54m

    • 99Introduction to Coding Neural Networks with PyTorch and Lightning21m
    • 100Long Short-Term Memory with PyTorch + Lightning33m

  6. Day 34 · 32m

    • 101Word Embedding in PyTorch + Lightning32m

  7. Day 35 · 49m

    • 102Coding a ChatGPT Like Transformer From Scratch in PyTorch31m
    • 103Reinforcement Learning: Essential Concepts18m
Week 6Videos 104–106 · 1h 7m

  1. Day 36 · 49m

    • 104Reinforcement Learning with Neural Networks: Essential Concepts24m
    • 105Reinforcement Learning with Neural Networks: Mathematical Details25m

  2. Day 37 · 18m

    • 106Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!18m

Tips for finishing

  • Use it alongside a hands-on course; StatQuest explains the why, and coding courses show the how.
  • The playlist isn't strictly ordered by difficulty. Pick a topic block (trees, boosting, neural networks) and finish it before moving on.

Frequently asked questions

How long does it take to finish StatQuest Machine Learning?

The playlist has 106 videos and 29.8 hours of content. At 1 hour a day that's about 30 days at normal speed, or 20 days at 1.5x. At 2 hours a day it's about 15 days.

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