Forecasting Time Series in Python | Buy here

  • A new textbook on forecasting time series. We develop forecasting approaches from classical methods, all the way through deep learning to Auto-ML and time series foundation models.Download your copy from the shop.
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    • Mastering Data Science Interviews | Buy here

  • A new guide to passing data science interviews and landing that sought after job! In over 800 pages I provide detailed answers to over 160 common questions in data science and machine learning interviews. We start off with a short succinct answer to each question and then dive into the intuition behind each concept. A deep dive into each topic is given along with Python coding examples. Download your copy from the shop.
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  • Explore a sample of questions from each book part here.
  • The python code for the worked examples can be downloaded from github.

    • Table of Contents:

    Introduction

    1. 1 The Aim of This Book
    2. 2 Prepare for Data Science Interviews

    I Modelling and Theory

    1. 3 The Bias–Variance trade-off
    2. 4 Parametric and Non-Parametric Models
    3. 5 Linear Regression
    4. 6 Multicollinearity and Model Stability
    5. 7 Logistic Regression
    6. 8 Decision Trees
    7. 9 Bagging and Random Forest
    8. 10 Boosting
    9. 11 Neural Networks

    II Optimisation and Training

    1. 12 Loss Functions
    2. 13 Gradient Descent
    3. 14 Momentum and Adaptive Optimisers
    4. 15 Regularisation
    5. 16 Training Instabilities
    6. 17 Weight Initialisation
    7. 18 Hyperparameters
    8. 19 Early Stopping

    III Evaluation and Metrics

    1. 20 Train, Validation, and Test Data
    2. 21 Data Leakage
    3. 22 Cross Validation
    4. 23 Classification Metrics
    5. 24 Confusion Matrices
    6. 25 ROC Curves
    7. 26 Precision–Recall Curves
    8. 27 Regression Metrics
    9. 28 Model Fitting
    10. 29 Model Comparison
    11. 30 Information Criteria
    12. 31 Statistical Testing
    13. 32 Calibration
    14. 33 Log Loss

    IV Conceptual Foundations

    1. 34 The Central Limit Theorem
    2. 35 Independence
    3. 36 Maximum Likelihood Estimation
    4. 37 Bayesian Inference
    5. 38 Probability Foundations
    6. 39 Feature Engineering
    7. 40 Feature Scaling
    8. 41 Curse of Dimensionality
    9. 42 Principal Component Analysis
    10. 43 Clustering
    11. 44 Generative vs Discriminative Models
    12. 45 Model Interpretability

    V Applied ML and Systems

    1. 46 Machine Learning Pipelines
    2. 47 Real-World Data Challenges
    3. 48 Missing Data
    4. 49 Outliers
    5. 50 Imbalanced Datasets
    6. 51 Model Deployment and Monitoring
    7. 52 Production ML Systems
    8. 53 Online Learning
    9. 54 Scaling Machine Learning
    10. 55 Distributed Training
    11. 56 Experimentation
    12. 57 Designing ML Systems

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