Fake news is a complex problem and can span text, images and video. For written articles in particular, there are several ways of generating fake news. A fake news article could be produced by selectively editing facts, including people's names, dates or statistics. An article could also be completely fabricated with made-up events or people. Fake news articles can also be machine-generated as advances in artificial intelligence make it particularly easy to generate misinformation.

Convince the business, in terms they can understand. Imagine you have been selected as a proponent to bring artificial intelligence into your business. Are you going to talk to executives about algorithms, training data, test data, supervised learning, unsupervised learning, and deep learning neural networks? Watch their eyes glaze over. There are plenty of AI efforts underway -- the global AI adoption rate grew steadily and now is 35%, a four-point increase from the year before, a recent IBM study shows.

Convince the business, in terms they can understand. There are plenty of artificial intelligence efforts underway -- the global AI adoption rate grew steadily and now is 35%, a four-point increase from the year before, a recent IBM study shows. It's clear, as shown in the study, that there are tangible benefits -- half of organizations are seeing benefits from using AI to automate IT, business or network processes, including cost savings and efficiencies (54%), improvements in IT or network performance (53%), and better experiences for customers (48%). But bringing in AI means changes for the organization, and the systems and processes that have defined the way business is done. Selling AI as a new way requires some degree of finesse and business sense.

Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.

In engineering disciplines, design patterns capture best practices and solutions to commonly occurring problems. They codify the knowledge and experience of experts into advice that all practitioners can follow. This book is a catalog of machine learning design patterns that we have observed in the course of working with hundreds of machine learning teams. Who Is This Book For? Introductory machine learning books usually focus on the what and how of machine learning (ML). They then explain the mathematical aspects of new methods from AI research labs and teach how to use AI frameworks to implement these methods.

Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus. The new specialization, called "Machine Learning with TensorFlow on Google Cloud Platform," has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

Recently I completed the Data Engineering on Google Cloud Platform Specialization (link here) through Coursera, here is my review. Only problem was a couple of issues in the final labs of the course. You can take each module out of order or complete sequentially. Its up to you, I'd recommend to keep it sequential at least roughly. I went from 1 to 3 then went back to 2, 4 and then 5. The courses are hosted by Valliappa Lakshmanan from Google.

Even though they're barely on the road, self-driving cars have been talked about so much that they already seem like they're last year's model. Google has been working on one for years. Apple is allegedly, possibly, working on one, too. And there's even speculation that everyone from Uber to Tesla could join the race, too. But before you give up the wheel, get familiar with the technology driving autonomous vehicles.