Organizations are turning to machine learning (ML) and artificial intelligence (AI) as a path to innovation and product and service differentiation, but too many organizations are stalled at the proof-of-concept stage. Register to attend and you'll learn: Join us on Feb 28 11 AM PT/2 PM ET to learn how to take a more industrialized and automated approach to developing and deploying ML and DL models across your enterprise.

Have you ever wanted to build a games with a graphical interface but didn't know how to? May be you even know how to create tools on a command line but have no idea how to convert it into a graphical interface that people can click on. In this course we will be learning Python GUI Programming Turtle other advanced python modules to build graphical user interfaces (GUI) and games from scratch. We will learn from basics of Python i.e. variables, slicing, string, some module, arithmetic and logical operations, looping, functions, object oriented programming. After that we will learn the basics stuff of Pygame and OpenGL and Blender basics stuff.

AI For Everyone will launch on February 28th! This non-technical course will teach you the language of AI, how to plan and execute successful AI projects, and how to drive AI adoption in your company. This course is taught by deeplearning.ai

Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns. In this course we are first going to talk about clustering.

It shouldn't just fall on businesses to address the talent shortage issue – universities have a role to play too. It is encouraging to see that universities are adding more machine learning and data science courses every day, with some making these new disciplines part of core curricula for certain degrees. But it shouldn't stop there – while academia provides students with theoretical training, enterprises can provide insight and experience based on real-world business problems. Businesses should step in by working with universities to help students gain practical, on-the-job experience. One way of doing this is to make work experience a course requirement. Progressive universities make it compulsory for students taking these courses to spend a semester working for a company in a relevant field.

Learn Convolutional Neural Networks for Visual Recognition and the building blocks and methods associated with them. Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases.

Here is a video lecture by Google's Director of Research - Peter Norvig. The full title of this lecture is "Theorizing from Data: Avoiding the Capital Mistake". In 1891 Sir Arthur Conan Doyle said that "it is a capital mistake to theorize before one has data." These words still remain true today. In this talk Peter gives insight into what large amounts of data can do for problems in language understanding, translation and information extraction.

Here is a video lecture by Google's Director of Research - Peter Norvig. The full title of this lecture is "Theorizing from Data: Avoiding the Capital Mistake". In 1891 Sir Arthur Conan Doyle said that "it is a capital mistake to theorize before one has data." These words still remain true today. In this talk Peter gives insight into what large amounts of data can do for problems in language understanding, translation and information extraction.

My latest data science project involved predicting the sales of each product in a particular store. There were several ways I could approach the problem. But no matter which model I used, my accuracy score would not improve. I figured out the problem after spending some time inspecting the data – outliers! This is a commonly overlooked mistake we tend to make.

Education was traditionally seen as an enabler of social mobility. In other words, if you were from a low-income family, you could improve your financial and social standing by getting an education. And for a while, it worked, but inequality is on the rise again. These days, college and university degrees are, at least in developed countries, a dime a dozen and you need a postgraduate qualification to get an entry-level position. The advantage of education to boost social mobility is more noticeable in developing countries where the demand for highly educated individuals outstrips the supply.