13. Automated Machine Learning by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters.
To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
14. Machine Learning Yearning by Andrew Ng
In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. Recommendations for how to set up dev/test sets have been changing as Machine Learning is moving toward bigger datasets, and this explains how you should do it for modern ML projects.
After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project. But your teammates might not understand why you’re recommending a particular direction. Perhaps you want your team to define a single-number evaluation metric, but they aren’t convinced.
15. Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn’t explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
16. Interpretable Machine Learning by Christoph Molnar
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees and linear regression. The focus of the book is on model-agnostic methods for interpreting black box models. Some model-agnostic methods like LIME and Shapley values can be used to explain individual predictions, while other methods like permutation feature importance and accumulated local effects can be used to get insights about the more general relations between features and predictions. In addition, the book presents methods specific to deep neural networks.
17. A Brief Introduction to Machine Learning for Engineers by Osvaldo Simeone
A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.
The book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.
18. Computer vision:models, learning and inference by Simon J.D. Prince
Topics explored include:
- Probability.
- Machine Learning for machine vision.
- Connecting local models.
- Preprocessing.
- Models for geometry.
- Models for vision.
Pages in this article:
Page 1: Understanding Machine Learning and more books
Page 2: Foundations of Data Science and more books
Page 3: Automated Machine Learning and more books