Computational Sciences Major
Computational sciences provide the scientific foundations for making sense of natural, human-mediated and social phenomena through analytics, computational methods and modeling.
In an age of ubiquitous — often overwhelming — data, the ability to harness that data to reflect, reach out and make better decisions is increasingly crucial. The Computational Sciences major prepares students to be analytics-driven and logical decision makers, innovators, and leaders.
The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation.
As the demand for machine learning and artificial intelligence goes up, leading tech giants realised the need to give developers access to tools to build and deploy models. From the industrial perspective, there aren’t enough skilled programmers and data scientists within the industry to develop these systems. Tech giants are now open sourcing their platforms and developer tools to lower the barrier for entry in AI/ML.
In this article, we list down 5 such tools that are making ML and AI accessible:
Convolutional neural networks are neural networks used primarily to classify images (i.e. name what they see), cluster images by similarity (photo search), and perform object recognition within scenes. For example, convolutional neural networks (ConvNets or CNNs) are used to identify faces, individuals, street signs, tumors, platypuses (platypi?) and many other aspects of visual data.
Deep Learning is becoming a very popular subset of machine learning due to its high level of performance across many types of data. A great way to use deep learning to classify images is to build a convolutional neural network (CNN). The Keras library in Python makes it pretty simple to build a CNN.