I have been looking to create this list for a while now. There are many people on quora who ask me how I started in the data science field. And so I wanted to create this reference. To be frank, when I first started learning it all looked very utopian and out of the world. The Andrew Ng course felt like black magic.

Learn how to build a sentiment analysis solution for social media analytics by bringing real-time Twitter events into Azure Event Hubs. In this scenario, you write an Azure Stream Analytics query to analyze the data. Then you either store the results for later use or use a dashboard and Power BI to provide insights in real time. Social media analytics tools help organizations understand trending topics. Trending topics are subjects and attitudes that have a high volume of posts in social media.

Machine learning was the fifth and latest guide. And now I'm back to conclude this series with even more resources. For each of the five major guides in this series, I spent several hours trying to identify every online course for the subject in question, extracting key bits of information from their syllabi and reviews, and compiling their ratings. My goal was to identify the three best courses available for each subject and present them to you. The 13 supplemental topics -- like databases, big data, and general software engineering -- didn't have enough courses to justify full guides. But over the past eight months, I kept track of them as I came across them. I also scoured the internet for courses I may have missed. For these tasks, I turned to none other than the open source Class Central community, and its database of thousands of course ratings and reviews.

Philip, who was underweight when he arrived, says to Adam, who is overweight: "I'm worried that you're not eating healthily. I noticed you've been skipping dinner." Adam is meant to repeat back to Philip what he heard him say the problem is. He mumbles, barely audible, and can't seem to remember what he's just been told. He's unable to focus, and the air is thick with reluctance and embarrassment.

Chatbots were supposed to be a big part of our AI-powered future, but they've mostly fallen flat. Scratch the surface of any online bot selling you takeout or flights abroad, and you'll usually find drop-down menus repackaged as questions. To get chatbots to the next level (and make them genuinely useful), they'll need to be given new skills -- like memory, and the ability to reason. Adding these new cognitive abilities is closer than you think. Facebook is one of the biggest players in this domain.

Facebook's chatbots are learning the art of the deal, bartering and deceiving their way to better terms in negotiations with humans and other bots. Artificial intelligences that can negotiate effectively would make useful virtual assistants, says Mike Lewis at Facebook's research lab. Bots could be left to arrange appointments for people, sorting out calendar clashes by themselves. Or they could negotiate with several agents at once to book a holiday or make a purchase on your behalf. Most existing bots – such as Apple's Siri or those built into chat apps like Facebook Messenger – may be able to get you a taxi or order a pizza but they can't engage in complex negotiations, says Lewis.

Something quite spectacular happened during the week: Students have achieved an astounding level of score improvement on a highly complicated machine learning problem - in just three afternoons. They achieved scores that improved more than 70% over the initial solution that were built by a team of experienced domain specialists and senior data scientists (figure 1). Considering that roughly half of the students had no prior exposure to machine learning, and that the other half were mostly beginners, these improvements are impressive. In fact, this is not the first time we observed this kind of results: every time we ran a data challenge using RAMP (rapid analytics and model prototyping) platform, major improvements have been made over the initial solution. So, how does this happen?

A business school in Paris will soon begin using artificial intelligence and facial analysis to determine whether students are paying attention in class. The software, called Nestor, will be used two online classes at the ESG business school beginning in September. LCA Learning, the company that created Nestor, presented the technology at an event at the United Nations in New York last week. Source: This French school is using facial recognition to find out when students aren't paying attention – The Verge This system will be used during videos to create quizzes based on when a student isn't paying attention. I don't understand the purpose since if they aren't paying attention a quiz isn't going to help them learn the material.

This article is from Scikits learn. Scikit-learn Machine Learning in Python is simple and efficient tools for data mining and data analysis. You can check all this information, here.

Every new technology goes through an adoption cycle, either in a society or inside one particular organization. Whether the process goes smoothly or not, depends on company's readiness to accept radical change. The transformation may seem complicated and unclear at first. However, it becomes substantially easier to adapt to change pursuing a number of preconditioned rules. InData Labs experts have a lot of experience and many years of practice in guiding companies within different industries through the process of AI-transformation.