Machine Learning in Banking

Machine learning, neural networks, regression, SVM, naive bayes classifier, bagging, boosting, random forest classifier.

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science

Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement Learning in Keras and TensorFlow

Learn and master the Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science

Build, train, and deploy a machine learning model using SageMaker ; Data Wrangling for Machine Learning on AWS Cloud

Proficiency in applying Machine Learning techniques using the scikit-learn, TensorFlow, and Keras

This book, the second one of three volumes, gives practical examples by a number of use cases showing how to take first steps in the digital journey of banks and insurance companies. The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between. This second volume mainly emphasizes use cases as well as the methods and technologies applied to drive digital transformation (such as processes, leveraging computational power and machine learning models).

State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions