Table of Contents
Part 1: Introduction. The big picture of artificial intelligence and machine
learning — past, present, and future.
Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear
regression, loss functions, overfitting, and gradient descent.
Part 2.2: Supervi...
Part 1: Introduction. The big picture of artificial intelligence and machine
learning — past, present, and future.
Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear
regression, loss functions, overfitting, and gradient descent.
Part 2.2: Supervised Learning II. Two methods of classification: logistic regression
and support vector machines (SVMs).
Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors,
decision trees, random forests. Introducing cross-validation, hyperparameter tuning,
and ensemble models.
Part 3: Unsupervised Learning. Clustering: k-means, hierarchical. Dimensionality
reduction: principal components analysis (PCA), singular value decomposition (SVD).
Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning
works. Drawing inspiration from the brain. Convolutional neural networks (CNNs),
recurrent neural networks (RNNs). Real-world applications.
Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision
processes. Q-learning, policy learning, and deep reinforcement learning. The value
learning problem.
Appendix: The Best Machine Learning Resources. A curated list of resources
for creating your machine learning curriculum.
Published August 19, 2017. Edited by Sachin Maini.
For inquiries regarding this book, please contact ml4humans@gmail.com
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Simple, plain-English explanations accompanied by math, code, and real-world examples
,Machine Learning for Humans
Part 1: Introduction.
Who should read this?
Technical people who want to get up to speed on machine learning quickly
Non-technical people who want a primer on machine learning and are willing
to engage with technical concepts
Anyone who is curious about how machines think
This guide is intended to be accessible to anyone. Basic concepts in probability,
statistics, programming, linear algebra, and calculus will be discussed, but it isn’t
necessary to have prior knowledge of them to gain value from this series.
If you're more interested in figuring out which courses to take, textbooks to read,
projects to attempt, etc. Take a look at our top picks in the Appendix: The Best
Machine Learning Resources.
Why machine learning matters
Artificial intelligence will shape our future more powerfully than any other innovation
this century. Anyone who does not understand it will soon find themselves feeling left
behind, waking up in a world full of technology that feels more and more like magic.
The rate of acceleration is already astounding. After a couple of AI winters and periods
of false hope over the past four decades, rapid advances in data storage and computer
processing power have dramatically changed the game in recent years.
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Part 1: Introduction
, Machine Learning for Humans
In 2015, Google trained a conversational agent (AI) that could not only convincingly
interact with humans as a tech support helpdesk, but also discuss morality, express
opinions, and answer general facts-based questions.
(Vinyals & Le, 2017)
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Part 1: Introduction
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