Have you ever wondered:
If you have and you want to learn the science behind them, you have come to the right place. In this course, I will show you how these companies use Recommender systems or Machine Learning to influence your purchasing decisions. This course is timely and extremely relevant now as almost all major service-oriented companies function on recommender systems.
You will understand how these systems work and learn how to build and use your own recommender systems, just like these big companies do.
Learn how to build the recommender systems that are being used by almost every big service-oriented company in today’s world with this introductory course for beginners.
Recommender Systems have changed the way people find products, information, and even other people. They study patterns of behavior to know what someone will prefer from among a collection of things they have never experienced. The technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders. Such systems are being used by companies such as Amazon, Facebook, Netflix, LinkedIn, Quora, Udemy, New York Times, etc. By taking this course, you will learn the most important of those tools, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice. The algorithms you will study include popularity-based systems, classification-based approach, collaborative filtering, matrix recommendation, etc.
I am a Scientist at IBM Research - Australia, working with the TrueNorth team on Machine Learning and its applications to mobile processors. I completed my Ph.D. from the Nanyang Technological University, Singapore on Neural Networks and Deep Learning. Like most researchers, I juggle between performing research and teaching students. My research involves developing novel algorithms that will be applied to automatically program the IBM's revolutionary neural network computer i.e. the TrueNorth system. I have published over 20 papers in international journals and conferences and you can check out my most cited papers below.
On the other hand, I love to teach my students the things I work on. After getting excellent feedback from the students I teach at the local universities, I decided to cater to the huge online community. My motto is to teach Machine Learning in a simple way such that it does not seem difficult and becomes accessible to everyone. I believe that behind each successful Machine Learning algorithm there is a physical significance or intuition that makes it work. Mathematics is required only to validate it. This thought pushed me into the path of creating and publishing courses related to Machine Learning, Big Data and Neural Networks where I shall discover and share those intuitions with you.
Please don't hesitate to drop me a message if you have a suggestion for a topic for one of my courses, or need help with something. I would love to talk to you.
My selected publications:
1. S. Roy and A. Basu, "An Online Structural Plasticity Rule for Generating Better Reservoirs", Neural Computation, MIT Press, 2016.
2. S. Roy and A. Basu, "An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, 2016.
3. S. Roy, P. P. San, S. Hussain, L. W. Wei and A. Basu, "Learning Spike time codes through Morphological Learning with Binary Synapses," IEEE Transactions on Neural Networks and Learning Systems, 2015.
4. S. Roy, A. Banerjee and A. Basu, "Liquid State Machine with Dendritically Enhanced Readout for Low-Power, Neuromorphic VLSI Implementations," IEEE Transactions on Biomedical Circuits and Systems, vol. 8, pp. 681–695, Oct. 2014.
5. S. M. Islam, S. Das, S. Ghosh, S. Roy and P. N. Suganthan, "An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization," IEEE Transactions on Systems, Man, and Cybernetics – Part B, vol. 42, no. 2, pp. 482-500, 2012.
I am a Machine Learning engineer and have a taken lot of courses on this topic. Recommender systems constitute one of the key sub-fields of Machine Learning. The way this instructor teaches this subject is really unique. It is one of the best Machine Learning courses in Udemy. He shows numerous real world examples to explain his point. The slides are equally interesting. The well-placed quizzes further reinforce the concepts. I have skimmed through the e-book and found that it contains a lot of information.
Very clear explanations and good examples. The instructor is very knowledgeable and goes straight to the point. I feel that a lot of thought went into the preparation of this course.
I maintain an e-commerce website for selling e-books. For the past few months I have been struggling to suggest good books to my existing and new customers. The instructor shows recent techniques used by the likes of Netflix, Amazon, etc. and tells us how to design them. This was a very informative course and I will be trying to build a recommender system for my website based on the things taught in this course. Thanks.
Great info on how to start out in the topic of Recommender Systems. I am certain that it is hard to find all that info in one spot. Explanations make a lot of sense and I am sure that I will complete 100% of this course. The instructor is very interactive and responsive.