Digital Marketing Certfication Course

An important issue confronting retailers and other businesses today is the preponderance of credit card fraud. This issue recently hit home, as my son was a victim a week prior to me writing this. We can apply machine learning to help detect credit card fraud, but there is a bit of a problem in that the vast majority of transactions are perfectly legitimate, which reduces a typical model’s sensitivity to fraud. As an example, consider a logistic algorithm running against the Credit Card Fraud dataset posted on Kaggle.   You can download it here: To follow along you will need an installation of Python with the following packages: NumPy Pandas SciKit-Learn You can get all those packages, and many more, with the Anaconda installation which you can find at: https://www.anaconda.com/download/ To begin with, start off with the necessary imports. importnumpyasnp importpandasaspd fromsklearn.metricsimportconfusion_matrix, cohen_kappa_score fromsklearn.metricsimport

The business that gets there first won’t necessarily win digital and AI game. It will be the one that ingrains digital and AI in its business as much as possible. Starting from applying intelligent data science where it matters most and progressively using it in every aspect of the business. In the modern banking environment, consumers are well informed. They expect intuitive, engaging and informative experiences when they bank. Banks need solutions that can help them delight their customers with personalized experiences, empower their workforce to provide differentiated experiences, optimize risk-taking capabilities with AI-enabled insights and transform products and services with data at the core. Applied data science and cloud-native business architecture are both critical for digital transformation for banks. At the center of this transformation is the data scientist and the supporting team of data engineers, data stewards and, depending on the size of the organization, spea