Machine Learning is without a doubt the core aspect of data science and predicative analytics in general. Its intuitive, versatile and robust approach to finding patterns in the available data makes it a priceless asset for anyone who wants to turn data into insights. What’s more, today it is more accessible than ever before, thanks to the variety of libraries in the programming languages used for Machine Learning and predictive modelling. This is particularly true for R, the open-source programming platform that specializes in this kind of tasks.
Up until relatively recently, R has been thought of a tool for statisticians, mainly because it handles statistical models proficiently through its various statistical tools. However, recently it has been upgraded through the introduction of a variety of libraries that contain efficient implementations of several Machine Learning algorithms. In addition, the introduction of parallelization libraries enabled R to run on computer clusters, where big data dwells. What’s more, due to its open-source license, R has attracted several practitioners who developed workshops, tutorials, etc. making learning it easier than anything else in the Data Science field.
Machine Learning is also quite accessible today, due to the variety of books on it. However, most of them have underlining assumptions about what you know, plus they give a lot of emphasis either on the programming aspect of it or on the mathematical dimension of the methods covered. It’s very difficult to find a resource that explains the ideas behind the algorithms and walks you through their implementation and the interpretation of their results, without getting overly technical.
What many people tend to forget is that Machine Learning can be quite enjoyable too. This is because it allows for a great deal of creativity in both the development of new algorithms as well as the implementation (and tweaking) of the existing ones. Plus all that results into getting a computer to do something intelligent that can provide value for you and your organization, bringing about a sense of accomplishment. What’s more, Machine Learning can hone your problem-solving skills and turn difficult problems into intriguing challenges that can be very educational too.
Naturally, learning Machine Learning is also about employability. Today, as more and more organizations become aware of the value of data analytics (esp. in a Data Science setting), the need for Machine Learning practitioners has exceeded the demand for it. This is why there are so many books on the topic as well as a variety of university courses. However, unless you are very methodical and have lots of time, reading books won’t cut it, especially if you are looking for a job in the field sometime soon. Besides, you can’t put books on a resume. As for the university courses, these too take time plus they are often quite pricey. For this and all the other aforementioned reasons, the School of Data Science has put forward a 2-day Machine Learning workshop, an efficient way to learn the essential aspects of the field, using the R platform, at a very reasonable price.
The idea of this and all other similar learning programs developed by School of Data Science is to make Machine Learning accessible to everyone who wants to get into it, without spending months on it (you can go in more depth afterwards, on your own if you want). Familiarizing yourself with the basic concepts and getting some experience on how they are applied will enable you to get into the field faster plus it will spur your enthusiasm about this fascinating field.
This workshop aims to develop basic understanding of Machine Learning based on supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression. With a focus on the former, it takes a close look at typical Machine Learning techniques and how they apply on datasets akin to those encountered in the real world.
Our goal is to give you the basic skills that you need to understand supervised machine learning algorithms and models, and interpret their output, which is important for solving a range of data science problems. This is an applied Machine Learning course, and we focus on the intuitions and practical know-how needed to get Machine Learning algorithms to work in practice, rather than the mathematical equations and derivatives.
Great opportunity for programmers, business analysts, technology consultants and all mortals interested in Machine Learning to learn several methods for building Machine Learning applications that solve different real-world tasks. Lots of hands-on labs to step through real-world applications of Machine Learning.
Read and download the workshop brochure.
Special Offer – 30% Discount!
Please take a moment to register now and avail the special 30% discount offered. Visit the event page and use the promo code MLBR30 to get 30% off.
So, join us for a packed, holistic, and enjoyable workshop this August and let yourself embark into an educational adventure in the world of Machine Learning. If you have any questions about the workshop or registration please feel free to contact me or email at email@example.com.
Happy Machine Learning!
Dr. Zacharias Voulgarisread more