In this article, we explore the various recommendation systems and implement Matrix Factorization with ML.NET.
From Netflix, Google, and Amazon, to smaller webshops, recommendation systems are everywhere. In fact, this type of system represents probably one of the most successful business applications of Machine Learning. Their ability to predict what users would like to read, watch and buy proved to be good not only for the business but for the users as well. For users, they provide a way to explore product space and for businesses they provide an increase in user engagement and more knowledge about the customers. Also, these systems are widespread and existing in almost every big cloud platform. When we think of YouTube video recommendations, they are there. Netflix menus with suggested series, they are turning the wheels behind the scene. Gmap suggested routes? You can bet. These systems became one of the building blocks of our industry and it would be bad not to know anything about them. In this article, we get familiar with these systems and see how we can build one using ML.NET .
The topics covered in this article are:
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?
In Conversation With Dr Suman Sanyal, NIIT University,he shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.