By the end of the course, you'll have the keys to the entire R Machine Learning Kingdom!

What can (and can't) be done with it? How to start creating AI methods? Join the Elements of AI course, online and free, created by Reaktor, an AI and tech partner for modern businesses and the University of Helsinki, the oldest institution of academic education in Finland. The course combines theory with practical exercises and can be completed at your own pace. At the end of the course you will also receive a certificate of participation.

Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn how to engineer features and build more powerful machine learning models. Who is this course for? So, you've made your first steps into data science, you know the most commonly used prediction models, you perhaps even built a linear regression or a classification tree model. At this stage you're probably starting to encounter some challenges - you realize that your data set is dirty, there are lots of values missing, some variables contain labels instead of numbers, others do not meet the assumptions of the models, and on top of everything you wonder whether this is the right way to code things up.

We invite you to watch on-demand webinars, join AWS experts as they share best practices and technical demos on building a robust analytics and data architecture. Our e-books and whitepapers are also designed to inspire and empower you. Here, you can explore topics on database, data lakes, analytics, data streaming, and machine learning.

Sign in to report inappropriate content. Machine Learning - How it Works - Part 2: https://youtu.be/umt4DJV04LE This video cover the basic concepts of machine learning and demonstrate them in action. If machine learning seems like mysterious hype to you, come learn how it actually works under the covers. Takeaway: I understand the basics of machine learning technology.

Last year, Finland launched a free online crash course in artificial intelligence with the aim of educating its citizens about the new technology. Now, as a Christmas present to the world, the European nation is making the six week program available for anyone to take. Finland is relinquishing the EU's rotating presidency at the end of the year, and decided to translate its course into every EU language as a gift to citizens. But there aren't any geographical restrictions as to who can take the course, so really it's to the world's benefit. The course certainly proved itself in Finland, with more than 1 percent of the Nordic nation's 5.5 million citizens signing up.

In technical aspects, Python is a high-level, object-oriented programming language that comes with integrated dynamic semantics primarily for app and web development. Though it's often compared to JavaScript, Ruby etc, the major thing that sets apart Python programming language from others is its simplicity. First released in 1991, Python has become one of the most popular programming languages across the globe. The huge popularity of Python programming language heavily relies on the unique features offered by it. Let's have a look at the features of Python that set it apart from other programming languages.

According to The Wall Street Journal, virtual simulations incorporating artificial intelligence (AI) are being used to improve health care workers' bedside manner. Doctors, nurses and other caregivers in southern Maine are being trained in a virtual environment that simulates the conditions that patients with Alzheimer's disease, cancer and substance addictions experience. At the Hospice of Southern Maine in Scarborough, health care workers wear virtual reality headsets and are able to move their hands, pick up objects and get a panoramic view of the hospice setting as if they are the patient. The patient's family--in a simulated form--is present at the virtual bedside. Such examples demonstrate the remarkable advances in skills development and training, thanks to technology such as artificial intelligence--when machines learn based on experience--and virtual and augmented reality.

Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch.