For example, a new customer support ticket with email threads between a customer and a CSR arrive and the machine learning model would predict a categorization and tell you how confident it was about that particular prediction. The media coverage seems to imply that AI is only the domain of the technology elite – companies such as Amazon Apple, Facebook, Google, IBM, Microsoft, Salesforce, Tesla, Uber – who can afford to assemble large teams of machine learning experts and invest $100M. But if a business did this without thinking about how it would also get high quality, high volume customized training data from which the machine learning model could learn you would have a mismatch between expectations ("we have a great algorithm") to outcome ("our model is only 60% accurate"). So over time the model can handle an increasing percentage of the customer support ticket classification work and the business can greatly increase the volume of tickets it classifies.

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

A few years ago, Jason Freeman was confronted by a classic hiring challenge. The 10-person startup he had founded, an online commercial real estate service called 42Floors.com, Suddenly, it seemed, Freedman, who had myriad other duties as CEO, was spending hours at a time sifting through towering stacks of résumés. The solution appeared in the form of artificial intelligence software from a young company called Interviewed. It speeds the vetting process by providing online simulations of what applicants might do on their first day as an employee. The software does much more than grade multiple-choice questions.

Summary: A great story about an AI-powered massive on-line open learning platform focused on STEM education. The platform and its content is to be available across many languages to serve students anywhere in preparing for a better life in STEM careers. If you're from the US you're probably feeling some angst as our K-12 students seem to slip further and further back on STEM studies. Imagine how bad it is in the lesser developed countries where shortages of STEM teachers and basic tech resources make it almost impossible for young people to prepare for a better life through a tech career. Worse still, UNESCO says there are 100 million young people around the world who do not attend school at all.

Last year, in the height of the election season, the Obama administration quietly released a national strategic plan for artificial intelligence (AI) research and development. The plan was the beginning of a national effort to prepare Americans for a future with AI--a future some computer scientist believe our nation is ill-equipped to handle. AI has become a part of the American fabric for some time. Siri and Alexa are already taking orders, self-driving cars have hit some streets, and the concept of interconnectivity is now a reality through the Internet of Things. But experts assert that in order for the society to fully embrace AI, learning machines should not replace human workers, but complement them.

The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation that allows for other types of deep learning problems to be considered.

If you want something with a Python heavy, Check out this book "Think Stats" This a great MOOC's to learn basic statistics needed for Data science:

The path of learning about Artificial Intelligence is often overwhelming with complex math and technical topics. But it doesn't have to be like that… We want to break that trend by creating an intuitive and exciting course which will guide you into the exploding World of AI and where you will have fun at the same time: Right this very moment we are running a Kickstarter Project to create a revolutionary training program on Artificial Intelligence. In this blog we are going to describe the secrets behind the structure of the course so even if you aren't ready to join this training – you can replicate these steps in your own learning program. One of the simplest AI algorithms is called Q-Learning. Simple but powerful, we will use it to train a robot like R2D2 to findits way out of a maze.

Machines are eating humans' jobs talents. And it's not just about jobs that are repetitive and low-skill. Automation, robotics, algorithms and artificial intelligence (AI) in recent times have shown they can do equal or sometimes even better work than humans who are dermatologists, insurance claims adjusters, lawyers, seismic testers in oil fields, sports journalists and financial reporters, crew members on guided-missile destroyers, hiring managers, psychological testers, retail salespeople, and border patrol agents. Moreover, there is growing anxiety that technology developments on the near horizon will crush the jobs of the millions who drive cars and trucks, analyze medical tests and data, perform middle management chores, dispense medicine, trade stocks and evaluate markets, fight on battlefields, perform government functions, and even replace those who program software – that is, the creators of algorithms. People will create the jobs of the future, not simply train for them, ...

This question is particularly relevant for supervised training problems. The typical premise underlying such problems is that a small high-quality dataset (say N entities) can help your model approximate an underlying function, which can generalize to your entire dataset (1000N entities). The allure of these approaches, of course, is that humans do the hard work on a small amount of data, and machines learn to replicate the work for a wider range of examples. In the real world though, problems don't always have an underlying pattern that can be identified. Humans draw on external general knowledge to solve cognitive challenges more often than we realize, which often leads us to falsely expect our algorithms to be able to solve the same challenges, without the benefit of the general knowledge that we posses.