Covid-19 pandemic has ignited an unprecedented risk for the economy. Banks and financial institutions across the globe are expected to register unusually high default rates on loans once the moratorium and forbearance imposed by the governments and the regulators are lifted. Scientific tools such as collection and recovery scorecards offer a mechanism to predict defaults
How data science will shape post-COVID banking? had a thought-provoking discussion with FrankBanker: 02:01 (Part 1) Impact on variables in Credit Models05:33 (Part 2) Are we going back to Judgemental Lending?07:50 (Part 3) Evaluating analytics readiness of Banks12:20 (Part 4) Is ‘IT’ the right place for Data Analytics?14:15 (Part 5) Changes in the Credit Scoring methodologies20:47
How will artificial intelligence and machine learning transform businesses? In this introductory part of the talk, learn how artificial intelligence will play a pivotal role to resolve conflicts within businesses.
This question and answer (Q&A) session will explore these topics: Identification of career opportunities in data science, machine learning, and Artificial Intelligence for beginners What to expect while starting your career in data science? How to make your career transition to data science, as an experienced professional, a smooth endeavor? What are the right strategies
Welcome back to the deep learning example to build an OCR application. The idea of this simple application is to identify numbers in an image of written text. In the last part, we used three different models and got the following accuracy for identification of the test images: Model 1 – Logistic regression: 92% accuracy
Facebook was a major sensation and a source of great amusement in a British country house in the early 20th century. It was such a big hit that it got a special mention in a newspaper published in the year 1902. Facebook, then, of course, had a completely different meaning than the online social media we
Welcome back to this series of articles on deep learning and neural networks. In the last part, you learned how training a deep learning network is similar to a plumbing job. This time you will learn the math of deep learning. We will continue to use the plumbing analogy to simplify the seemingly complicated math. I
The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. Notably, Apple’s Siri, Google-DeepMinds’ AlphaGo, or the self-driving mechanism in Tesla cars are all based on deep learning. Here, my goal is to make deep learning neural networks much more accessible for everyone. In this series
In the last part of this series on fundamental machine learning, you learned about regularization and cross-validation. Here, you will gain a sound understanding of model hyper-parameter tuning to develop robust models. The machines do learn but they still need a good human tutor. In the last part, you were also introduced to my paternal grandmother to
In the previous article, we started with the theme that overfitting is an inherent problem in machine learning associated with big data. Essentially, if you have many variables and their polynomial terms (X-variables) in a model you could fit any response data (y-variable) to perfection. This perfect fit for the observed data is overfitting since this model will