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Interpretable machine learning is a genuine concern to stakeholders across the domain. No longer an esoteric consternation, or a "nice to have" for practitioners, the importance of interpretable machine learning and AI has been made known to more and more people over the past number of years for a wide array of different reasons. All of this could leave one wondering: where does one go to find a cache of quality reading material for learning such an important issue? Enter Interpretable Machine Learning, a free eBook by Christoph Molnar. First, what is the motivation for the book?

Data Science is recognized as the sexiest job of the 21st century. As organizations seek to maintain themselves through data-driven insights, demand for data science professionals is soaring relentlessly. Many reports show that the demand for data scientists is growing year over year and continues to rise sharply. Thus, to harness insights in their data to personalize experiences at scale, companies need to acquire the best data professionals. On the other side, before entering a data science career, candidates need to grasp knowledge into the world of data and analytics.

Automatic behavior monitoring technologies are becoming part of our everyday lives thanks to advances in sensors and machine learning. The automatic analysis and understanding of behavior are being applied to solve problems in several fields, including health care, sports, marketing, ecology, security, and psychology, to name a few. Behavior Analysis with Machine Learning and R aims to provide an introduction to machine learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in databases. The book covers topics within the entire data analysis pipeline--from data collection, visualization, preprocessing, and encoding to model training and evaluation. No prior knowledge in machine learning is assumed.

I readily admit that I'm biased toward Python. This isn't intentional -- such is the case with many biases -- but coming from a computer science background and having been programming since a very young age, I have naturally tended towards general purpose programming languages (Java, C, C, Python, etc.). This is the major reason that Python books and resources are at the forefront of my radar, recommendations, and reviews. Obviously, however, not all data scientists are in this same position, given that there are innumerable paths to data science. Given that, and since R is powerful and popular programming language for a large swath of data scientists, today let's take a look at a book which uses R as a tool to implement solutions to data science problems.

Time to get back to basics. This week we have a look at a book on foundational machine learning concepts, Understanding Machine Learning: From Theory to Algorithms.

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.

It's no secret that mathematics is the foundation of machine learning, and is vital to your understanding of the underpinnings of the field. In order to succeed as a machine learning practitioner, knowledge of the applicable mathematical foundations are absolutely necessary. Where can you turn to brush up on your machine learning maths, or strengthen your understanding by extending that base? Mathematics for Machine Learning is a book currently in development by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, with the goal of motivating people to learn mathematical concepts, and which is set to be published by Cambridge University Press. According to the authors, the goal of the text is to provide the necessary mathematical skills to subsequently read books on more advanced machine learning topics.

You may have also used or heard of their equally high quality deep learning, machine learning, linear algebra, and natural language processing courses. It has also been a major protagonist in the development of transfer learning for natural language processing; performed an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. This library provides easier API access to a variety of machine learning-related functionality, especially when it comes to neural networks. Much of this aspect of the library sits atop PyTorch, making the creation of neural networks with this lower level library easier and flexible for machine learning coders of all skill levels. As a bridge between their courseware and the fastai library which it uses, Jeremey Howard and Sylvain Gugger are working on a book titled Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD, which is not yet available.

It's a new week, and what better time to get your hands on another free eBook? We have been highlighting a new such installment weekly for the better part of the past few months, doing our best to single out and share top learning materials for those stuck at home right now, or really for anyone interested in learning a new concept or brushing up on what they already know. This week we turn our attention to the topic of automated machine learning (AutoML), a personal favorite of mine. What is automated machine learning? It is a wide (and widening) concept, but I've previously tried to capture its essence as such: If, as Sebastian Raschka has described it, computer programming is about automation, and machine learning is "all about automating automation," then automated machine learning is "the automation of automating automation."