Machine learning book recommendations The Hundred-Page Machine Learning Book by Andriy Burkov - Best machine learning overview Machine Learning For Absolute Beginners by Oliver Theobald - Best for absolute beginners Machine Learning for Hackers by Drew Conway and John Myles White - Best for programmers (who enjoy practical case studies) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien - Best for those who know Python Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville - Best book on deep learning An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani - Best for a statistics approach Programming Collective Intelligence by Toby Segaran - Best guide for practical application Fundamentals of Machine Learning for Predictive Data Analytics by John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy - Best for an analytics approach Machine Learning for Humans by Vishal Maini and Samer Sabri - Best for a free resource Source: 9 Machine Learning Books for Beginners: a 2025 Guide - Coursera ...

I recently came across an interview with the late Stanford neurosurgeon James Doty that reframed something many dismiss as vague or unscientific: manifestation. Doty reframes “manifestation” not as wishful thinking, but as a disciplined practice rooted in self-agency and neuroscience. He dismantles the idea that outcomes are delivered by some external force, and instead shows how intentions can be embedded into the subconscious through structured practices: writing them down, speaking them aloud, visualising outcomes, and deliberately reshaping internal narratives that otherwise hold people back. ...

Introduction to Markov Chains Given the present, the future is independent of the past Further reading Introducing Markov chains - Harvard Memorylessness