R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. If you want to learn reward-based learning with R, then you should surely go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Beginning with the basics of R programming, this Learning Path provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. You will be able to boost your productivity with the most popular R packages and data structures such as matrices, lists, and factors.

The program was created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). It is also closely related to Yandex School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. In the faculty, learning is based on practice and projects. National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more.

After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs. Before I turn to Udacity advanced courses, I want to mention one thing at the beginning. If I could give advice to myself, I would select another introduction course on Deep Learning -- Deep Learning Specialization by Andrew Ng. First of all, his way of mentoring is unique and he can explain complex things in most clear and understandable way.

If you've ever wanted Jetsons to be real, well we aren't that far off from a future like that. If you've ever chatted with automated robots, then you've definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it's reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist.

THIS IS GONNA BE A OVER 40 HOUR OF CONTENT COURSE! This is Your Complete Guide to mastering statistical modelling, data visualization, machine learning and basic deep learning in R. BOOST YOUR CAREER TO THE NEXT LEVEL: This course covers ALL the aspects of practical data science, which makes this course The Only Data Science Training You Need. By the end of the course, you'll be able to store, filter, manage, and manipulate data in R to give yourself & your company a competitive edge. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

Do you want to step into the ever-growing field of data science? Do you wish to equip yourself with one of the most widely used language for data science? Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Data is on the rise and it's the need of the hour to process it and make sense out it. Analysts and statisticians need to get this job done. It's an art to tactfully and efficiently process data.

This year, the world witnessed significant technology advancements that will have long term implications for our economy and the way we work. Artificial intelligence dominated the list of top courses this year, taking three of the spots in our top 10 list, including the Machine Learning and Deep Learning courses taught by our co-founder Andrew Ng and a Machine Learning course from the University of Toronto. Blockchain has also burst onto the scene, putting Princeton's Bitcoin and Cryptocurrency course at number five on the list.

About this course: The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn't produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on live examples.

The teaching of this course is done making the use of the "inverted classroom" model. This in simple terms means that instead of being introduced to the related material in a large lecture hall that limits itself to one-way communication, one can first watch the lecture that has been recorded by Geoffrey Hinton as a set of about 3 short videos at home before the commencement of the class, and then in class, takes place a much more dynamic discussion about it. If one is already registered for the class, you will be able to view all these videos on the Coursera website. Further details of how to do this will be given in the first lecture period.