How to: Machine Learning
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1. Book, Aurélien Géron,"Hands-On Machine Learning with Scikit-Learn and TensorFlow"
1. [Foundations of Machine Learning](https://bloomberg.github.io/foml/)
1. [Best Resources to Learn Machine Learning](http://www.trainindatablog.com/best-resources-to-learn-machine-learning/)
1. [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow)
1. ["Papers with Code" - Browse the State-of-the-Art in Machine Learning](https://paperswithcode.com/sota)
1. [Zhi-Hua Zhou. 2012. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC.](https://www.amazon.com/exec/obidos/ASIN/1439830037/acmorg-20)
1. [Feature Engineering for Machine Learning. Principles and Techniques for Data Scientists. By Alice Zheng, Amanda Casari](https://www.amazon.com/Feature-Engineering-Machine-Learning-Principles-ebook/dp/B07BNX4MWC)
1. [Google Research: Looking Back at 2019, and Forward to 2020 and Beyond](https://ai.googleblog.com/2020/01/google-research-looking-back-at-2019.html)
1. [O’Reilly: The road to Software 2.0](https://www.oreilly.com/radar/the-road-to-software-2-0/)
1. [Machine Learning and Data Science Applications in Industry](https://github.com/firmai/industry-machine-learning)
1. [Deep Learning for Anomaly Detection](https://ff12.fastforwardlabs.com/)
1. [Federated Learning for Mobile Keyboard Prediction](https://arxiv.org/pdf/1811.03604.pdf)
1. [Federated Learning. Building better products with on-device data and privacy on default](https://federated.withgoogle.com/)
1. [Federated Learning: Collaborative Machine Learning without Centralized Training Data](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) 
1. [Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T. and Yu, H., 2019. Federated learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 13(3). Chapters 1 and 2.](https://www.morganclaypoolpublishers.com/catalog_Orig/samples/9781681736983_sample.pdf)
1. [Federated Learning by FastForward](https://federated.fastforwardlabs.com/)
1. [THE FEDERATED & DISTRIBUTED MACHINE LEARNING CONFERENCE](https://www.federatedlearningconference.com/)
1. [Federated Learning: Challenges, Methods, and Future Directions](https://blog.ml.cmu.edu/2019/11/12/federated-learning-challenges-methods-and-future-directions/)
1. [Book: Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable", 2019](https://christophm.github.io/interpretable-ml-book/)
1. [Book: Hutter, Frank, Lars Kotthoff, and Joaquin Vanschoren. "Automated Machine Learning". Springer,2019.](https://originalstatic.aminer.cn/misc/pdf/Hutter-AutoML_Book_compressed.pdf)
1. [ML resources by topic, curated by the community. ](https://madewithml.com/topics/)
1. [An Introduction to Machine Learning Interpretability, by Patrick Hall, Navdeep Gill, 2nd Edition. O'Reilly 2019](https://learning.oreilly.com/library/view/an-introduction-to/9781098115487/)
1. [Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.](https://github.com/jphall663/interpretable_machine_learning_with_python)
1. [Paper: "Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence", by Sebastian Raschka, Joshua Patterson, and Corey Nolet. 2020](https://arxiv.org/pdf/2002.04803.pdf)
1. [Distill: Machine Learning Research](https://distill.pub/)
1. [AtHomeWithAI: Curated Resource List by DeepMind](https://storage.googleapis.com/deepmind-media/research/New_AtHomeWithAI%20resources.pdf)
1. [Awesome Data Science](https://github.com/academic/awesome-datascience)
1. [Intro to probabilistic programming. A use case using Tensorflow-Probability (TFP)](https://towardsdatascience.com/intro-to-probabilistic-programming-b47c4e926ec5)
1. [Dive into Snorkel: Weak-Superversion on German Texts. inovex Blog](https://www.inovex.de/blog/snorkel-weak-superversion-german-texts/)
1. [Dive into Deep Learning. An interactive deep learning book with code, math, and discussions. Provides NumPy/MXNet, PyTorch, and TensorFlow implementations](http://d2l.ai/)
1. [Data Science Collected Resources (GitHub repository)](https://github.com/tirthajyoti/Data-science-best-resources)
1. [Set of illustrated Machine Learning cheatsheets](https://stanford.edu/~shervine/teaching/cs-229/)
1. ["Machine Learning Bookcamp" by Alexey Grigorev](https://www.manning.com/books/machine-learning-bookcamp)
1. [130 Machine Learning Projects Solved and Explained](https://medium.com/the-innovation/130-machine-learning-projects-solved-and-explained-605d188fb392)
1. [Machine learning cheat sheet](https://github.com/soulmachine/machine-learning-cheat-sheet)
1. [Stateoftheart AI. An open-data and free platform built by the research community to facilitate the collaborative development of AI](https://www.stateoftheart.ai/)
1. [Online Machine Learning Courses: 202