Data science

Projects

• I used machine learning models to predict optimum emission reduction strategies to improve air quality and public health in China.
  • These were Gaussian process emulators trained from ~20 TB of simulated data from numerical atmospheric models.
  • I provided training for these emulators video (Colab, slides, and GitHub).
• I created an online course on Introduction to Machine Learning.
  • It covers fundamentals, machine learning with scikit-learn, deep learning with TensorFlow / Keras and PyTorch / PyTorch Lightning, data pipelines, model tuning, transfer learning, and distributed training.

Resources

• Foundations
  • Computational and Inferential Thinking: The Foundations of Data Science, Ani Adhikari and John DeNero, Data 8: Foundations of Data Science course, UC Berkeley.
    • Video lectures.
  • Python Data Science Handbook, Jake VanderPlas, 2016.
• Experiments:
  • Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing, Ron Kohavi, Diane Tang, and Ya Xu.

Machine Learning

If you’re interested in jobs in this area, I highly recommend Workera to help figure out what the roles are, what you’re suited to, what you need to improve on, and personalised plans to make this progress.

Machine Learning

• Machine learning, Coursera, Andrew Ng.
  • Video lectures, CS229, Standford University.
• Machine Learning for Intelligent Systems, Kilian Weinberger, 2018.
  • CS4780, Cornell: Video lectures.
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, Aurélien Géron, 2019, O’Reilly Media, Inc.
  • Jupyter Notebooks
• MLExpert.
• Machine Learning Yearning, Andrew Ng.

Deep Learning

• Deep Learning Specialization, Coursera, DeepLearning.AI.
  • Video lectures, CS230, Stanford University.
  • Syllabus, CS230, Stanford University.
• NYU Deep Learning, Yann LeCun and Alfredo Canziani, NYU, 2021.
  • Video lectures.
• Physics-based Deep Learning, Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, Kiwon Um, 2021.

Artificial Intelligence

• Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell and Peter Norvig, 2021, Pearson.
• Artificial Intelligence: Principles and Techniques, Percy Liang and Dorsa Sadigh, CS221, Standord, 2019.

Maths

• Linear Algebra, Gilbert Strang, MIT 18.06, 2005.
• Essence of linear algebra, 3Blue1Brown.
• Essence of calculus, 3Blue1Brown.
• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Gilbert Strang, MIT 18.065, 2018.

MLOps / ML Engineering

• Machine Learning Engineering for Production (MLOps) Specialization, Coursera, DeepLearning.AI.
• Production Machine Learning Systems, Google Cloud, Coursera.
• Full Stack Data Science.
• Designing Machine Learning Systems, Chip Huyen, 2022.
• Effective Data Science Infrastructure, Ville Tuulos, 2022.

Causal Inference

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions, Miguel Hernan, Harvard University.
• Introduction to Causal Inference, Brady Neal.
  • Video lectures.