This game-changing course will cover artificial intelligence tools in content creation, curation, augmented reality and digital marketing and will take you on a glimpse into the future. We will also look at influencer marketing tools, content trends and a bit of competitor analysis through the use of BuzzSumo. Why learn this course and how is this a differentiator for content creators? This course can change your life if you are a content expert. Because, I will provide you with a hands-on experience on creating tons and tons of articles for your blog for inbound marketing using an Artificial Intelligence content tool and you don't even have to write the content yourself - ever again.

Deep Learning models often perform significantly better than traditional machine learning algorithms in many tasks. This course consists of hands-on recipes to use deep learning in the context of supervised and unsupervised learning tasks. After covering the basics of working with TensorFlow, it shows you how to perform the traditional machine learning tasks in supervised learning: regression and classification. This course also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning. To address many different use cases, this product presents recipes for both the low-level API (TensorFlow core) as well as the high-level APIs (tf.contrib.lean

NLP, or Natural Language Processing, is a computational approach to communication. This course will get you up-and-running with the popular NLP platform called Natural Language Toolkit (NLTK) in no time. You will start off by preparing text for Natural Language Processing by cleaning and simplifying it. Then you will implement more complex algorithms to break this text down and uncover contextual relationships that reveal the meaning and content of the text. You will learn how to tokenize various parts of sentences, and how to analyze them.

GigaSpaces' Director of Solution Architectures and Professional Services, Rajiv Shah, will be speaking about How to Operationalize AI for Instant Business Impact with Hybrid Transactional and Analytical Processing at the upcoming Intel AI DevCon 2018 in San Francisco on May 24th, 2018. Make plans to join us May 23 – 24, 2018, at The Palace of Fine Arts in the Marina District of San Francisco. Real-time applications and business systems require instant data processing, analysis and the ability to leverage insight instantly for immediate action. In this session, you will learn how the convergence of work-flows and technology platforms for real-time, analytics, cloud, and in-memory processing allows organizations to effectively address time-sensitive business decisions that involve burgeoning volumes of big data while benefiting from the velocities of real-time processing. Case studies will be presented on how AI and Machine Learning can be operationalized for price optimizations, fraud detection, risk calculations, and operational business intelligence.

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very very good guess about stock prices movement in the market. In the first chapter we are going to talk about the basics of the R programming language.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Confused whether artificial intelligence and machine learning are the same thing? Anna Brown asks the experts to explain. LEARN MORE ABOUT ARTIFICIAL INTELLIGENCE https://www.sas.com/en_us/insights/an... SAS DOES AI - CHECK OUT SAS AI SOLUTIONS https://www.sas.com/en_us/solutions/a... LEARN MORE ABOUT MACHINE LEARNING https://www.sas.com/en_us/insights/an... WEBINAR: INTRODUCTION TO MACHINE LEARNING In this this webinar, Wayne Thompson of SAS delves into those issues and provides an overview of machine learning, as well as key business applications of this technique, including fraud detection, model factories and recommendation systems. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 75,000 sites make better decisions faster. Since 1976, SAS has been giving customers around the world THE POWER TO KNOW .

In SQL Server 2016, you have to the ability to integrate R for in-database, scalable machine learning. We will explore some of the challenges faced when pushing your algorithms and results to a consumable format. This is the first course in a series of courses to be released.

These articles are between 3 and 5 year old, but are still valuable today. The methodology used in these articles is modern, and still state-of-the-art today. Some discuss immense data sets still available to the public, and that resulted in designing new machine learning techniques to handle them. I am in the process of organizing these articles (written by myself) to eventually self-publish data science tutorials, in a few separate booklets, that are easy to understand for the layman with one year of data camp or college education in data science. The material will eventually be accessible to Data Science Central members, but not published in a traditional book. My writing style has evolved over time: I have moved away from writing academic papers long ago, to most recently share advanced knowledge in a way that is accessible to beginners, sometimes even ground-breaking material, such as this one.