I am relatively new to predictive modeling techniques and would like to get a few concepts cleared/discussed. I am currently in the process of building a logistic regression model using Weight of Evidence (WOE) technique. I understand that the log odds and WOEs tend to have a linear relationship - a pre-requisite for the model. In case of categorical variables, WOEs can be used to make them continuous. But what if, the Log odds have a U-shaped relationship with the independent variable.

Machine learning automation is affecting all of enterprise software, but will completely transform how we build, analyze, and consume data and analytics. Tableau, Qlik, Tibco Spotfire) have disrupted the traditional BI market (e.g. Yet, as transformative as these tools have been, analytics is once again at a critical inflection point. Across the analytics stack, tools have become easier to use and more agile, enabling greater access and self-service. And yet organizations' processes for preparing data for analysis, analyzing data, building advanced analytics models, interpreting results and telling stories with data remain largely manual and prone to bias.

Consumers have spoken, artificial intelligence is a profitable industry. From Amazon to Google to Apple, major tech companies have made inroads, crafting intelligent software -- housed in sleek, accessible hardware -- that has drawn massive customer attention. This trend is set to soon move out of home devices, like Echo and Google Home, and onto the streets, where self-driving cars leverage major breakthroughs in computer vision so passengers can ride easy, knowing their vehicles will "see" and react to objects and road signs in real time without their input. In fact, cars with these features are already popular with consumers, and by 2020 10 million cars with self-driving attributes will be on roadways. But while there are plenty of ways for consumers to leverage AI, enterprises are asking themselves how they can get in on this wave of innovation. And a big part of the answer lies at the crossroads of computer vision and an emerging field known as transfer learning.

Everyone who follows the tech space knows that artificial intelligence ("AI") is one of the hottest area of investment. From my anecdotal experience, if you ask market participants to list who they think is the current leader in AI, most will say Google (NASDAQ:GOOGL) (GOOG), then followed by perhaps Microsoft (MSFT) or NVIDIA (NVDA). Being labeled an "AI" company helps these stocks attract incremental investors. However, it surprises me how few market participants recognize Amazon's (AMZN) leadership in AI. In my view, though impossible to quantify exactly, Amazon's AI investments and capabilities should sustain and increase its competitive advantage over time, leading me to believe that the stock has a much longer runway than what investors are giving it credit for. If I am correct, this also means bad news for Amazon competitors - this is particularly true in the retail space where competitive have invested little in AI technology by comparison.

About this course: Course Description What is our role in the universe as human agents capable of knowledge? What makes us intelligent cognitive agents seemingly endowed with consciousness? This is the second part of the course'Philosophy and the Sciences', dedicated to Philosophy of the Cognitive Sciences. Scientific research across the cognitive sciences has raised pressing questions for philosophers. The goal of this course is to introduce you to some of the main areas and topics at the key juncture between philosophy and the cognitive sciences.

Machine learning already plays a big part in your everyday life, and its role is only going to grow. Google searches and muttered requests to Amazon's Alexa may tap into a veiled world of clever algorithms, but these techniques teeter on something much larger: a world of self-developing artificial intelligence. Deep learning, and the neural networks that do the thinking, is becoming an integral seam to digital technology. By extension, artificial intelligence is having a growing effect on our experience of the world and, as an artist, it is a material that can't be ignored. That at least is the thinking of Anna Ridler, who is building a name for herself with works that hoist machine-learning techniques and bring them into the gallery.

About this course: Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.

According to a new study published in the journal Consciousness and Cognition, consumption of a low amount of alcohol facilitated problem-solving skills and boosted creative thinking. Later, when the participants were given tasks related to word associations-- like linking the words "swiss", "blue" and "cake"-- it was found that those who had consumed the alcoholic beer were more likely to make the guess that "cheese" was the correct linking word. Thereby, alcohol may facilitate a broader associative search and the effective solving of creative tasks that are prone to fixation effects." "We wanted to do this study because alcohol is so linked with creativity and great writers like Ernest Hemingway.

The opioid epidemic has become one of the worst health crises in US history. In 2015, more than 90 Americans died every day from opioid overdoses, a number comparable to deaths in car accidents and projected to have risen further in 2016 and 2017. The problem often begins with legitimate healthcare encounters in which opioid painkillers are first prescribed, such as for surgeries or chronic back pain. During treatment, some patients become addicted and go on to suffer the well-documented consequences of addiction, while others do not, even if they become long-term users. To combat the epidemic, it is vital to understand the exact circumstances under which medically sanctioned treatments can devolve into addiction.

About this course: Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information. We start the first week by introducing some major systems for data analysis including Spark and the major frameworks and distributions of analytics applications including Hortonworks, Cloudera, and MapR. By the middle of week one we introduce the HDFS distributed and robust file system that is used in many applications like Hadoop and finish week one by exploring the powerful MapReduce programming model and how distributed operating systems like YARN and Mesos support a flexible and scalable environment for Big Data analytics.