Ted Dunning (Chief Application Architect at MapR) and Ellen Friedman have written a new O'Reilly Media book on _"Practical Machine Learning – Innovations in Recommendation" _(released in January 2014). This book examines one of the most interesting, fun, and powerful data science applications in the big data universe: recommendation systems. For me, this was one of the most interesting applications of data mining that immediately captured my imagination after I embarked on the journey to data science (drifting away from my astrophysics roots) about a dozen years ago. It is also one of the most common use cases that are taught in data science MOOCs and other analytics training courses. I believe that the love affair with recommender systems can be partly attributed to two things.

How do you interact with your customers? Given the predominance of mobile devices and messaging apps, many experts believe the next era of customer interaction will belong to "the conversational layer" -- both text- and voice-driven -- that will use chat, messaging, or natural language interfaces to interact with people, brands, services, and bots. In this webinar, Babson College's Bala Iyer, author of the MIT SMR article "Do You Have a Conversational Interface?," Using industry examples and findings from his research, he offers strategies for capitalizing on conversational interfaces to capture customer loyalty.

Andrew Ng's five courser aims to give newbies and practitioners a crash course on all things deep learning – from fully connected neural networks to convolutional nets to sequence models. I've taken all five courses, and completed four. For some more online course recommendations, check out the best online courses to get started with data science. The first course in the specialization focuses on the building blocks of deep learning. It goes over logistic regression interpreted as a one-layer network, shallow networks, and finally deep networks as stacked shallow networks. Well, if you've taken Andrew Ng's precursor course Machine Learning, then the first course in Deep Learning is basically just an elaboration of the neural network part.

Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2018, then you have come to the right place.

I recently had the pleasure of being invited to speak at The Item Conference http://www.item.nu/cgi-oic/pagedb.exe/show?no 1 in London for educators, policy-makers and head-teachers visiting our amazing City for inspiration and knowledge about how to foster creativity in children with I. T. They were particularly interested in the possibilities of Artificial Intelligence and Machine Learning. So, with thinking cap on, and just a few short hours to prepare, I was thrilled to find out that A.I in education is not the work of science fiction, but is with us right now -- in action, and starting to build impact. For hundreds of years, humans have pondered the idea of building intelligent machines. Over this time, artificial intelligence has had highs and lows, demonstrated successes and unfulfilled potential. Today, the news is filled with the application of AI and machine learning to new problems.

The algorithm to rule them all! That definitely sounds like a mega-nerdy thing to say that is far-fetched. However, many people in academia and industry are racing to find the algorithm that can generally solve many, many problems. It is still very much a theoretical problem, and one that can really spark the imagination if you just put some thought to it. I first heard about the coined term'The Master Algorithm' while driving back home listening to this podcast.

Work with me or attend my 2 day workshop! Here's a quick example of reading a shape file into R as simple features via st_read(), then plotting those features (in this case, North Carolina counties) using each one of the four mapping approaches plotly provides. You might be wondering, "What can plotly offer over other interactive mapping packages such as leaflet, mapview, mapedit, etc?". One big feature is the linked brushing framework, which works best when linking plotly together with other plotly graphs (i.e., only a subset of brushing features are supported when linking to other crosstalk-compatible htmlwidgets). Another is the ability to leverage the plotly.js

Microsoft today launched two new courses in its online education program for developers: an entry-level software development class and an AI course for more advanced developers who want to expand their knowledge of machine learning. It's no secret that there aren't enough data scientists and machine learning developers available to fulfill the current demand. It's no surprise, then, that a number of large companies have started to teach the fundamentals of these disciplines to their existing employees; starting today, anybody can take the AI courses that Microsoft first developed for its own employees. The Microsoft Professional Program for Artificial Intelligence is available for free on edX.org, though you can also opt to pay for a certificate. Each course runs three months and starts at the beginning of the quarter. Unsurprisingly, there's a bit of a focus on Azure and Microsoft's Cognitive Services here (and you need an Azure account), but otherwise the course is agnostic to the operating system you run.

Click here to register using the code UNDERWOOD and save $300 on an All Access Pass or $200 off a Conference Pass.] As machine learning algorithms are transparently weaved into business intelligence tools, automated decision-making processes, and the fabric of our day-to-day lives, it is becoming more important for everyone to understand the fundamentals. Machine learning is susceptible to a wide variety of bias types and a myriad of other issues if applied improperly. It also has amazing untapped potential when implemented correctly.

One of the things that's so fundamental in software development that it's easy to overlook is the idea of a repository of shared code. As programmers, libraries immediately make us more effective. In a sense, they change the problem solving process of programming. When using a library, we often think of programming in terms of building blocks -- or modules -- that can be tied together. How might a library look for a machine learning developer?