What do you think?
Rate this book
Machine Learning for Humans
Table of Contents
Part 1: Introduction. The big picture of arti cial intelligence and machine learning—past, present, and future.
Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, over tting, and gradient descent.
Part 2.2: Supervised Learning II. Two methods of classi cation: 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.
Part 1: Introduction. The big picture of arti cial intelligence and machine learning—past, present, and future.
Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, over tting, and gradient descent.
Part 2.2: Supervised Learning II. Two methods of classi cation: 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.
97 pages, ebook
Published September 2, 2017
19 people are currently reading
334 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 14 of 14 reviews
February 3, 2019
The author suggests three different approaches to consume this book; T-shaped approach, Focused approach and the 80/20 approach.
I chose the later, it was good enough to clarify the high-level concepts of artificial intelligence. I then watched videos about these high-level concepts (Supervised Learning, Unsupervised learnings, Neural Network) on a video learning platform to help me better understand.
There is little information about business applications and ethical aspects, if you are interested in these topics, I recommend reading a different book.
I chose the later, it was good enough to clarify the high-level concepts of artificial intelligence. I then watched videos about these high-level concepts (Supervised Learning, Unsupervised learnings, Neural Network) on a video learning platform to help me better understand.
There is little information about business applications and ethical aspects, if you are interested in these topics, I recommend reading a different book.
May 17, 2020
A very concise overview. The content is not as good as the one hundred machine learning book. But the recommended recourses are great! The online version might be better for all the hyperlinks: https://medium.com/machine-learning-f...
August 15, 2023
A great, approachable primer on Machine Learning. With the perfect amount of detailed and digestibility and additional resources, I recommend this to anyone who wants to learn about one of the technologies of the future: machine learning.
May 7, 2024
This is a high-level overview that I found comprehensible, and this would especially be the case for anyone with any statistical training in regression. The authors offer decent resources for further study. All and all, a good primer.
September 30, 2017
a quick couple hours read to understand basic ML concepts
January 15, 2018
A basic intro to machine learning with explanations in simple terms. Very recommended if you want a starting point in these topics.
May 3, 2018
Whirlwind tour of machine learning, with advanced math and a lot of concepts I had no chance at understanding first time through. Will serve as a good big picture as I become more familiar
June 30, 2018
I find it really easy to understand how Machine Learning works and when or how to use it . It not a easy book to read, it is the kind of book you have to read a little by little.
September 10, 2018
Good brief overview
April 1, 2019
This book is an amazing start point with a lot of references to courses, projects and answers to questions about machine learning.
Want to read
August 21, 2019It will be a nice book
May 9, 2020
Great book for a brief introduction to machine learning concepts
July 12, 2020
Very interesting read covering a lot of ground on a variety of topics. Serves as a good introduction to the field with lots of useful links
July 10, 2023
Quite interesting book regarding machine learning, to revisit the basis, explained in simple yet effective terms
Displaying 1 - 14 of 14 reviews