These courses teach you R programming and data science interactively, at your own pace, in the comfort of your browser.

It's 8:00 am on a Tuesday morning. You've awoken, scanned the headlines on your phone, responded to an online post, ordered a holiday sweater for your mom, locked up the house, and are driving to work, listening to some great new music on the radio. You've also used artificial intelligence (AI) more than a dozen times -- to be roused, to call up local weather report, to purchase a gift, to secure your house, to be alerted to an upcoming traffic jam, and even to identify an unfamiliar song. AI is already pervasive in our world, and it's making a huge difference in our everyday lives. But this is not the AI you've seen in sci-fi movies, with nervous scientists clacking on keyboards and attempting to halt machines from destroying the world. Sometimes it's obvious, like when you ask Siri to get you directions to the nearest gas station, or Facebook suggests a friend for you to tag in an image you posted online. Sometimes less so, like when you use your Amazon Echo to make an unusual purchase on your credit card (like that goofy holiday sweater) and don't get a fraud alert from your bank.

Put machine learning principles into practice to solve real-world problems Get to grips with Python's impressive range of Machine Learning libraries and frameworks From retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline Get to grips with Python's impressive range of Machine Learning libraries and frameworks Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. You'll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more.

The television show will be shot in both Canada and United States. The first Canadian episodes will be shot in Toronto on November 27, 2016. And the first US episodes will be shot on March 16 and 17 in 2017. These shows are going to be aired on (TBC) with an expected viewership of 3 million. Day one of the show will feature pitches by the artificial intelligence companies to the world of investors.

Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for R: This link contains a curated list of R tutorials for Data Science, NLP and Machine Learning.

Publishing is no longer about writing just a book. Therefore publishing is not just dependent on itself but on other mediums as well. A best selling book makes it to a Netflix show or a movie and that in turn gives the book sales another fillip. So content in its traditional form - i.e as a book is not dying anytime. In fact, the need for good content is greater than ever as players like Netflix and HBO keep on searching for the next bestselling story to be made into a tv show or a movie.

We've already published the top big data presentations on slideshare, as well as great Github list of public data sets, or top machine learning projects, or top R packages. We've asked our readers to share a list of top Data Science videos on YouTube. Here, we share a list of top data science presentations from VideoLectures.net. These presentations received 5 to 20 times fewer page views than those on Slideshare, because they are far more technical, and attract a different, truly technical audience. You can check the entire list here.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.

There are many Python machine learning resources freely available online. Go from zero to Python machine learning hero in 7 steps! The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best?

The Perceptron algorithm is the simplest type of artificial neural network. It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks. In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python. How to train the network weights for the Perceptron. How to implement the Perceptron algorithm for a real-world classification problem.