Machine Learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.

Hands-On Machine Learning with Scikit-Learn and TensorFlow
Pattern Recognition and Machine Learning (Information Science and Statistics)
Deep Learning
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Machine Learning: A Probabilistic Perspective
The Hundred-Page Machine Learning Book
Deep Learning with Python
Python Machine Learning
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
Machine Learning (McGraw-Hill International Editions Computer Science Series)
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
Information Theory, Inference, and Learning Algorithms
Artificial Intelligence: A Modern Approach
The Signal and the Noise by Nate SilverThe Elements of Statistical Learning by Trevor HastieMoneyball by Michael   LewisThe Visual Display of Quantitative Information by Edward R. TufteAn Introduction to Statistical Learning by Gareth James
Data Science - Learning About Data
133 books — 121 voters

eMortal by Steve  SchaferThe Singularity is Coming by Tony  ThorneTogether by Zoltan AndrejkovicsMachine Learning by Samuel HackRise of the Robots by Martin Ford
The next big thing in tech
34 books — 31 voters
The Elements of Statistical Learning by Trevor HastieProbability and Measure by Patrick BillingsleyFoundations of Modern Probability by Olav KallenbergAn Introduction to Probability Theory and Its Applications, V... by William FellerStatistical Inference by George Casella
Statistical Science Year 3 (MCSL)
41 books — 10 voters

James Rickards
... bias in training materials is inevitable and there are better ways to deal with adverse effects than to scrub them out of existence. Efforts to eliminate bias would simply create new kinds of bias and distort the validity of the original datasets. Bias can be unjust by some standards but serves a useful purpose. ... the way to deal with it is to use other systems developed by those not involved in the original code, assisted by subject matter experts who could spot damaging bias in AI output ...more
James Rickards, MoneyGPT: AI and the Threat to the Global Economy

A.R. Merrydew
Mastering the technology to create effigies of our ourselves, will be our downfall.
A.R. Merrydew

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