Books, papers and documentation
- 811sources
- 810identity verified
- 1not verified or restricted
- 27core reading
Statistics3
Fast, applied statistics reference for analysts. Most useful: ch. 1 Exploratory Data Analysis, ch. 2 Data and Sampling Distributions, ch. 3 Statistical Experiments and Significance Testing, ch. 4 Regression and Prediction.
textbookcore readingverifiedpaid book (O'Reilly)Industry standard for A/B testing. Most useful: ch. 3 Twyman's Law and Experimentation Trustworthiness, ch. 7 Metrics for Experimentation and the Overall Evaluation Criterion, ch. 14 Choosing a Randomization Unit, ch. 17 The Statistics behind Online Controlled Experiments, ch. 18 Variance Estimation and Improved Sensitivity.
textbookcore readingverifiedpaid book (Cambridge); chapter 1 free on experimentguide.comSection 1.2 for the missing-data mechanisms (Day 6, Day 9 imputation).
textbookverifiedfree online
Machine learning8
Primary classical-ML text with Python labs. Most useful: Resampling Methods (cross-validation, bootstrap), Linear Model Selection and Regularization, Tree-Based Methods, Classification, and the Multiple Testing chapter.
textbookcore readingverifiedfree PDF from the authors' site; paid print (Springer)Current HOML title (PyTorch replaces Keras/TensorFlow for the deep-learning half). Most useful: ch. 2 End-to-End Machine Learning Project, ch. 3 Classification, ch. 4 Training Models, ch. 6 Ensemble Learning and Random Forests.
textbookcore readingverifiedpaid book (O'Reilly); notebooks free on GitHubSame content as the Python edition with R labs; cite it only when the R labs are needed. Prefer book-isl-python for this academy.
textbookverifiedfree PDF on statlearning.com; paid print (Springer)Chapter 3 (Classification) for unawareness and redundant encodings.
textbookverifiedfree online; print edition from MIT PressKeras/TensorFlow edition; the Scikit-Learn chapters (ch. 2 End-to-End Machine Learning Project, ch. 3 Classification, ch. 7 Ensemble Learning and Random Forests) remain useful. Prefer book-homl-pytorch for new references.
textbookverifiedpaid book (O'Reilly); notebooks free on GitHubModel-agnostic interpretation done carefully. Most useful: ch. 17 Shapley Values, ch. 18 SHAP, ch. 19 Partial Dependence Plot, ch. 20 Accumulated Local Effects, ch. 23 Permutation Feature Importance.
textbookverifiedfree online edition; paid ebook/printDeeper theory behind ISL. Most useful: Model Assessment and Selection (bias-variance, cross-validation, bootstrap), Linear Methods for Regression, Boosting and Additive Trees, Random Forests.
textbookverifiedpaid book (Springer); authors' free PDF not checked (site unreachable)Learning-theory reference for why generalization works. Most useful: ch. 2 A Gentle Start, ch. 5 The Bias-Complexity Trade-off, ch. 6 The VC-Dimension, ch. 11 Model Selection and Validation, ch. 14 Stochastic Gradient Descent.
textbookverifiedpaid book (Cambridge); author-hosted PDF not checked (site unreachable)
Forecasting and time series4
Python version of FPP3 using StatsForecast/MLForecast/NeuralForecast; the main forecasting text for this Python-based academy. Most useful: ch. 5 The forecaster's toolbox, ch. 8 Exponential smoothing, ch. 9 ARIMA models, ch. 15 Foundation forecasting models.
textbookcore readingverifiedfree online edition; paid printStandard forecasting text (R/fable). Most useful: ch. 3 Time series decomposition, ch. 5 The forecaster's toolbox (benchmarks, residuals, accuracy, time-series CV), ch. 8 Exponential smoothing, ch. 9 ARIMA models.
textbookverifiedfree online edition; paid printStandard RM reference: demand forecasting, unconstraining, capacity control and overbooking; supports aviation/hotel forecasting cases (Days 26-29). Chapter-level mapping not checked.
textbookverifiedGraduate-level time-series theory. Most useful: Characteristics of Time Series, Time Series Regression and Exploratory Data Analysis, ARIMA Models, State-Space Models.
textbookverifiedpaid book (Springer)
Causality and experiments4
Stable 1st edition, applied econometrics flavor. Most useful: ch. 3 Directed Acyclic Graphs, ch. 4 Potential Outcomes Causal Model, ch. 9 Difference-in-Differences, ch. 10 Synthetic Control. A 2nd edition (book-mixtape-remix) is in progress.
textbookcore readingverifiedfree online edition; paid print (Yale)Rigorous potential-outcomes treatment. Most useful: Part I (causal inference without models: randomization, confounding, selection bias) and Part II (with models: IP weighting, standardization, g-methods). Section numbers can change between online versions.
textbookcore readingverifiedfree online edition (PDF)Updated 2nd edition with a stronger panel-data section: ch. 9 Difference-in-Differences Fundamentals, ch. 10 Complex Diff-in-Diff Designs, ch. 11 Synthetic Control. It is still being corrected, so check passages before citing.
textbookverifiedfree online edition (work in progress)Primary reference for structural causal models, DAGs, d-separation and the do-operator; cite it for formal definitions, not as first reading.
textbookverifiedpaid book (Cambridge)
Deep learning3
Clearest modern deep-learning text with fill-in notebooks. Most useful: the notebooks for chapters 6-9 (gradient descent, SGD, Adam; backpropagation and initialization; bias-variance and double descent; regularization).
textbookcore readingverifiedfree PDF; paid print (MIT Press)Foundational theory (pre-transformer). Most useful: ch. 5 Machine Learning Basics, ch. 6 Deep Feedforward Networks, ch. 7 Regularization for Deep Learning, ch. 8 Optimization for Training Deep Models, ch. 11 Practical Methodology.
textbookverifiedfree online HTML; paid print (MIT Press)Code-first deep learning with PyTorch. Most useful: ch. 5 Multilayer Perceptrons, ch. 11 Attention Mechanisms and Transformers, ch. 12 Optimization Algorithms, ch. 19 Hyperparameter Optimization.
textbookverifiedfree online edition with runnable notebooks; paid print
NLP and LLMs5
Main applied-LLM text. Most useful: ch. 3 Evaluation Methodology, ch. 4 Evaluate AI Systems, ch. 6 RAG and Agents, ch. 7 Finetuning, ch. 9 Inference Optimization.
textbookcore readingverifiedpaid book (O'Reilly)Builds a GPT-style model in PyTorch step by step. Most useful: ch. 2 Working with Text Data, ch. 3 Coding Attention Mechanisms, ch. 4 Implementing a GPT Model from Scratch, ch. 7 Finetuning to Follow Instructions.
textbookverifiedpaid book (Manning); code free on GitHubCh. 8 (evaluation in IR) for ranked-retrieval metrics; supports day 51.
textbookverifiedfree online editionHands-on Hugging Face Transformers usage. Most useful: ch. 2 Text Classification, ch. 3 Transformer Anatomy, ch. 8 Making Transformers Efficient in Production. Predates instruction tuning and RAG tooling.
textbookverifiedpaid book (O'Reilly)Draft that keeps changing; chapter numbers change between releases, so cite with the release date. In the Aug 2026 draft: ch. 2 Words and Tokens, ch. 5 Embeddings, ch. 7 Transformers and Pretraining, ch. 8 Post-training, ch. 11 Information Retrieval and RAG.
textbookverifiedfree online draft (PDF)
MLOps and engineering2
Production ML end to end. Most useful: ch. 4 Training Data, ch. 5 Feature Engineering (incl. leakage), ch. 6 Model Development and Offline Evaluation, ch. 8 Data Distribution Shifts and Monitoring, ch. 9 Continual Learning and Test in Production.
textbookcore readingverifiedpaid book (O'Reilly)Short, practical MLOps book. Most useful: ch. 2 Before the Project Starts, ch. 3 Data Collection and Preparation, ch. 7 Model Evaluation, ch. 9 Model Serving, Monitoring, and Maintenance.
textbookverifiedchapter drafts free on the book wiki; paid PDF/print
Visualization9
Practical chart-design rules. Most useful: ch. 4 Color scales, ch. 16 Visualizing uncertainty, ch. 17 The principle of proportional ink, ch. 23 Balance the data and the context, ch. 29 Telling a story and making a point.
textbookverifiedfree online edition (CC BY-NC-ND 4.0); paid print- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
- textbookverifiedfree online (CC BY-NC-ND 4.0)
Data-ink and graphical integrity (Day 8).
textbookverifiedbook (purchase)