This volume introduces working with text, with a focus on the most plentiful source of text out there: email. Working with email text from your own Gmail account, you will build up a label predictor, similar in effect to the technology Google uses to power the Social and Promotions tabs. With this technique, you will be able to build your own email classification and automated workflow hooks. Will Ballard serves as Chief Technology Officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the Executive Vice President of Technology and Engineering at Demand Media.

Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package. The course is ideal for undergraduate students seeking to master additional'in-demand' analytical job skills to offer a prospective employer.

About this course: The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.

Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead. The first project introduces natural language processing including part-of-speech tagging and named entity extraction. Wikipedia articles are used to demonstrate the extraction of keywords, and the Enron email archive is mined for mentions and relationships of people, places, and organizations.

Stanford has long been considered one of the best universities in terms of teaching, quality of faculty and the content they teach. With the recent boom in the machine learning field, Stanford's ML courses have generated a lot of interest (you can find videos on YouTube if you haven't done so already). Each year, Stanford releases a list of projects that it's students have worked on and recently, in that same regard, has released a list of course projects for it's Natural Language Processing (NLP) course. And wow, is it impressive. Students were given two options for the project – either choose your own topic (called'Custom Project') or take part in the'Default Project', which was building Question Answering models based on the SQuAD challenge.

Ideal for the aspiring developer, this collection boasts more than 30 hours of training on the technology that powers today's AI breakthroughs. Make your way through each of the four courses, and you'll foster new, practical knowledge in machine learning algorithms, AI applications, and more. Artificial Intelligence & Machine Learning Training -- This course offers a solid introduction to the current and potential applications of AI. You'll learn the basic ideas and techniques used in the design of intelligent computer systems, and, once you have your feet wet, you'll advance to statistical and decision-theoretic modeling paradigms, deep learning, and a host of other advanced topics. Introduction to Machine Learning -- In just two hours, this course will walk you through machine learning, the technology that powers self-driving cars, search engines, and more of today's AI breakthroughs.

Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions. Artificial Neural Networks are models loosely based on how neural networks work in a living being.

With the emergence of big data and modern technologies, artificial intelligence has acquired a lot of relevance in many domains. The increase in demand for automation has generated many applications for artificial intelligence in fields such as robotics, predictive analytics, finance, and many more. So, if you're a developer who wants to upgrade your normal applications to smart and intelligent versions, then go for this Learning Path. Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

You cannot avoid mathematical notation when reading the descriptions of machine learning methods. Often, all it takes is one term or one fragment of notation in an equation to completely derail your understanding of the entire procedure. This can be extremely frustrating, especially for machine learning beginners coming from the world of development. You can make great progress if you know a few basic areas of mathematical notation and some tricks for working through the description of machine learning methods in papers and books. In this tutorial, you will discover the basics of mathematical notation that you may come across when reading descriptions of techniques in machine learning.