Data analysing, irrespective of its form, can be extremely chaotic and challenging. This is where feature engineering steps in. A method to ease data analysis, feature engineering simplifies data reading for machine learning models. A feature or variable is nothing but the numerical representation of all kinds of data– structured and unstructured. Feature engineering is a vital part of the process of predictive modelling.

Image and face recognition platforms and solutions have been a major focus in the technology sector over the past two decades. Images and face recognition technology are used in many industries, including healthcare, security, e-commerce and security. This has resulted in remarkable progress. Experts believe this technology can perform at or even surpass human-level in many medical diagnoses and security domains. Many brands now use image recognition technology to harness the intersection of visual analytics and text to understand the industry and target audience, and to deploy visual intelligence to drive meaningful communications.

The emergence of artificial intelligence has proved its potential of disrupting every industry. The adjoining technologies along with AI have also become much-needed priorities in business processes to boost revenues and meet customer expectations. This rise in technologies like cloud computing has also brought with it a host of cloud computing jobs that tech aspirants are quite excited about. The emergence of cloud computing and SaaS has led to enhanced scalability since a potentially infinite number of users can access the cloud without any restriction. The top cloud computing jobs need qualified candidates who can handle these advanced technologies and tools.

With deep learning gaining its momentum in fields like self-driving cars, object detection, voice assistants and text generation, to name a few, the demand for deep learning experts in organisations has also significantly increased. As a matter of fact, big tech companies like Facebook, Google, Apple as well as Microsoft have started investing heavily on deep learning projects which, in turn, increase the number of deep learning open jobs in the market. Having said that, deep learning is one of the complex subsets of machine learning and envelops several layers of components which cannot be grasped in a day. Hence, despite the high demand, there is indeed a gap in deep learning talent for organisations. Not only does it come with prerequisites of linear algebra and calculus knowledge but also enough interest to pursue a complicated subject like deep learning.

The Smith School of Business at Queen's University has introduced Canada's very first Fintech Masters course. According to the Smith School of Business, the course has been designed mainly for financial or tech professionals who are already working in the industry. The first Master of Financial Innovation and Technology (MFIT) program will reportedly begin in November of this year. The courses will be delivered during the evening and there will also be some sessions held over the weekends. Graduates of the Fintech Masters program can expect to receive professional training in data science, finance, and emerging machine learning (ML) technologies.

Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques.

Google is silently churning out many new technologies with wide ranging and far reaching impacts. The AI platform that Google is working on for quite some time has already been integrated with its digital assistant called Google Assistant that has been successfully leveraged into smart speakers of all types including the Google's own Google Home. But, this was just the beginning and company is on the verge of delivering something new this time. Google made tremendous research in the fields like natural language processing and synthesis. Google for quite some time is working on a new technology that can allow people more ease without relying too much on the Assistant. The company is trying to come up with something that doesn't require every project to incorporate Google Assistant for voice based commands.