Banks are increasingly turning to machine learning to cope with stricter risk-modeling regulations. "Even if you have a simple econometric model which you can explain to the regulator, you can also use your machine learning model as an alternative model and say, 'OK, I have checked and tested my other model with this machine learning model,' " says Mostafa Mostafavi of Credit Suisse.
AB Consultants is a company that outsources its employees as Consultants to top various IT firms. Their business had been increasing quite well over past, however in recent times there has been a slowdown in terms of growth because their best and most experienced employees have started leaving the Company. Inorder to prevent this proactively you first need to dive in to the Company's Employee Data and find out an answer as to know why the best and most experienced employees are leaving. As a Data Analyst of the Company you are required do an analysis and find out patterns as to why the best employees are leaving so early. Using Python, you derive at a forecast model to predict which employees could be leaving the company, as well as a probability as to why our best and most experienced employees are leaving prematurely.
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this paper, we study the role of model usage in policy optimization both theoretically and empirically. We first formulate and analyze a model-based reinforcement learning algorithm with a guarantee of monotonic improvement at each step. In practice, this analysis is overly pessimistic and suggests that real off-policy data is always preferable to model-generated on-policy data, but we show that an empirical estimate of model generalization can be incorporated into such analysis to justify model usage. Motivated by this analysis, we then demonstrate that a simple procedure of using short model-generated rollouts branched from real data has the benefits of more complicated model-based algorithms without the usual pitfalls.
This course model will teach you how to teach, train and create Machine Learning models faster, easier without writing a single line of code. Just come as you are and leave with some understanding of Machine Learning. No prior knowledge is required. Even little children can teach and train a Machine Learning model. There are interesting links to help us understand how AI & ML is/are changing society, the pros and cons and so much more.
Get started today with a free Azure account! This repository contains a GitHub Action for registering Machine Learning Models with Azure Machine Learning model registry for use in deployment and testing. This action is designed to register models that may or may not have been trained using Azure Machine Learning. If they are not trained using Azure Machine Learning, we expect the model to be present in your GitHub Repository. Additionally, this action also supports model comparison, if the model has been created by an Azure Machine Learning (pipeline) run and is not stored in your repository.