Predictive modeling, or supervised machine learning, is a powerful tool for using data to make predictions about the world around us. Once you understand the basic ideas of supervised machine learning, the next step is to practice your skills so you know how to apply these techniques wisely and appropriately. In this course, you will work through four case studies using data from the real world; you will gain experience in exploratory data analysis, preparing data so it is ready for predictive modeling, training supervised machine learning models using tidymodels, and evaluating those models. To take this course, you need some familiarity with tidyverse packages like dplyr and ggplot2 and exposure to machine learning basics. Now let's get started!
Machine learning case study interview
AI Machine Learning for Fraud DetectionâCase Study | Cognizant
Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over leaders in machine learning and data science to understand what AI interviews are and how to prepare for them. AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. The interviewer is evaluating how you approach a real-world machine learning problem. The interview is usually a technical discussion of an open-ended question.
Artificial Intelligence & Machine Learning Case Studies
Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again.
The use of data to drive positive business results is widely debated and many companies have recognized the potential of artificial intelligence and machine learning in particular to implement powerful data-driven solutions. In practice, companies can benefit from a wide range of machine learning applications, but it is not always clear which solutions are the most feasible and efficient to achieve the desired results. The best techniques available to solve a problem vary depending on the goals, resources, and data of each business. To get a feel for how machine learning can create business value in different industries using different techniques, I have summarized some machine learning case studies that I have practically conducted.