The field of artificial neural networks has exploded during the 1980s due to its universal approximation capabilities, as can be seen in [1], but the lack of understanding of the underlying statistical and geometric features extracted from the analyzed signal discouraged significantly its usage among scientists and researchers, as can be seen in [2-3]. Since then, most of its usage has been relegated to applications where such understanding can be neglected, such as computer vision, nonlinear statespace estimators and other tasks related to control where exact algorithmic approaches are unknown or too difficult to implement, according to [3]. More recently, aiming to enlightening these black-boxes, several approaches have been under heavy development, such as variables contributions in the feed forward structure [4], visualization using saliency maps [5], generation of skeletal structures [6], fuzzy rule based evaluation of all permutations [3], extraction of functional relations using sensitivity analysis of input data [7], as many others. In a parallel way, other researchers have been successfully developing new kinds of feed-forward neural architectures that behave much more like a transparent box, where the extracted features can be directly evaluated and understood. Convolutional Neural Networks are a great example of such achievements, as can be seen in [8-10]. Despite its several layers, they can be employed on different types of tasks, including text classification, natural language processing, computer vision and so on, with a good understanding of what is happening behind the curtains. Manuscript received January 15, 2018. This work was supported in part by the FIPE (Institute of Economic Research Foundation) by means of a postdoctoral scholarship.

Have you ever wished you could simply think a command and your computer would respond? That's the future envisioned by Massachusetts Institute of Technology (MIT) researchers who created AlterEgo, a wearable system that allows you to converse with a computer without using your voice or movement. According to a video on the project from MIT Media Lab, the ultimate goal of AlterEgo is "to combine humans and computers." A computing system and wearable device comprise AlterEgo, a futuristic project led by graduate student Arnav Kapur of the Fluid Interfaces group at MIT. Electrodes, a machine learning system, and bone-conduction headphones help get the job done: the electrodes "pick up neuromuscular signals in the jaw and face that are triggered by internal verbalizations -- saying words'in your head' -- but are undetectable to the human eye," according to a MIT News statement. A machine learning system, trained to correspond certain signals with words, receives the signals. The bone-conduction headphones "transmit vibrations through the bones of the face to the inner ear."

In one season, it rains heavily, cities get flooded with water, and life comes to a halt. In another season, there is a scarcity of water and thousands of lives are affected every year with drought. Can I harvest the rain water and manage water scarcity? This is one student thinking differently, observing a problem, asking questions and challenging situations, developing a solution and creating an impact. As a society, we often discuss problems, share our views and opinions, but how many of us really contribute towards developing innovative solutions to address the problem?

"I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful." "By taking this course my perception has changed and now data science for me is more about data wrangling. Welcome to The Complete Course for Machine Learning Engineers.

Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media? Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends? Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Mining unstructured text data and social media is the latest frontier of machine learning and data science. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.

The mission of this course is to turn you into a productive, innovative data analyst who can leverage Go to build robust and valuable applications. To this end, the course clearly introduces the technical aspects of building predictive models in Go, but also helps you understand how machine learning workflows are applied in real-world scenarios. This course shows you how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives you patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization. You'll begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources.

Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the Globe is, "How do I get started in Machine Learning?" One reason could be the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This systematic guide will teach you various Machine Learning techniques. You will start with the very basics of neural networks and types. Then we learn about powerful variations in neural networks and Recurrent Neural Networks.

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

The aim of this course is to provide a practical understanding of advanced Machine Learning algorithms in Apache Spark to make predictions and recommendation and derive insights from large distributed datasets. This course starts with an introduction to the key concepts and data types that are fundamental to understanding distributed data processing and Machine Learning with Spark. Further to this, we provide practical recipes that demonstrate some of the most popular algorithms in Spark, leading to the creation of sophisticated Machine Learning pipelines and applications. The final sections are dedicated to more advanced use cases for Machine Learning: streaming, Natural Language Processing, and Deep Learning. In each section, we briefly establish the theoretical basis of the topic under discussion and then cement our understanding with practical use cases.

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. We will use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on.We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming.It focuses on the various tree-based machine learning models used by industry practitioners.We will also discuss k-nearest neighbors, Naive Bayes, Support Vector Machine and recommendation engine.By the end of the course, you will have mastered the required statistics for Machine Learning Algorithm and will be able to apply your new skills to any sort of industry problem. Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore.