This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. Enjoy the reading, or become one of our bloggers and start posting articles and tutorials on DSC.

Now that Christmas and the New Year are behind us the nights are becoming a little longer with each passing day. Nevertheless, there's still loads of cold winter nights left to endure (unless you're in the Southern Hemisphere, in which case – throw me a shrimp on the barbie!). It's time to dust off your New Year resolutions from last year (remember those?) and get ready for a new start, a new you and learn some new data skills. I've thrown together a collection of five excellent (and free!) Data Science eBooks for your Kindle to sharpen up your ninja skills while you're on the long commute to work. Just try not to read them while driving!

Data Science is one of the hottest jobs of the 21 century with an average salary of over $120,000. This course is designed learners of all backgrounds including beginners with no programming experience to experienced programmers who would like to advance to become a spatial data scientist. I will teach you programming with R to visualize, explore, and analyze your spatial data. At the end of this course, you will be able to acquire skills spatial data analysis. Enroll now in this course and start your journey of becoming a spatial data scientist!

Feeling rather good about myself as I'm writing this as I've just completed the first course of Andrew Ng's latest Deep Learning specialization on Coursera. I've been meaning to learn about Deep Learning for quite awhile now but haven't been able to wrap my heads around the theory aspect of it for longest of time. Previously, my foray into deep learning has been via Udacity's Deep Learning materials, random internet articles, and the Deep Learning textbook. Bought it from Amazon a few months ago, and am still going through the pages. Still finding it tough to find the time between going through a few pages, the day job, and sorting out the kids at night.

This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code.

This book was designed around major data structures, operations, and techniques in linear algebra that are directly relevant to machine learning algorithms. There are a lot of things you could learn about linear algebra, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials. I designed the tutorials to focus on how to get things done with linear algebra. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

Deep learning, also known as deep structured learning or hierarchical learning, is a type of machine learning focused on learning data representations and feature learning rather than individual or specific tasks. Feature learning, also known as representation learning, can be supervised, semi-supervised or unsupervised. Deep learning architectures include deep neural networks, deep belief networks and recurrent neural networks. Real-world applications using deep learning include computer vision, speech recognition, machine translation, natural language processing, and image recognition. The following recipe introduces how to implement a deep neural network using TensorFlow, which is an open source software library, originally developed at Google, for complex computation by constructing network graphs of mathematical operations and data (Abadi et al. 2016; Cheng et al. 2017).

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.

Abstract: As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, machine learning techniques are becoming an essential tool for real-world cyber security applications. One of the most important differences between cyber security and many other applications is the existence of malicious adversaries that actively adapt their behavior to make the existing learning models ineffective. Unfortunately, traditional learning techniques are insufficient to handle such adversarial problems directly. The adversaries adapt to the defender's reactions, and learning algorithms constructed based on the current training dataset degrades quickly. To address these concerns, we develop a game theoretic framework to model the sequential actions of the adversary and the defender, while both parties try to maximize their utilities.

Emerging anxieties pertaining to the rapid advancement and sophistication of artificial intelligence appear to be on a collision course with historic models of human exceptionality and individuality. Yet it is not just objective, technical sophistication in the development of AI that seems to cause this angst. It is also the linguistic treatment of machine "intelligence." But what is really at stake? Are we truly concerned that we will be surpassed in our capacities as human beings?