What Is DevOps? A Guide to the Basics
December 6, 2024
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Instructor: Epaminondas Kapetanios
1,568 already enrolled
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(20 reviews)
Recommended experience
Intermediate level
Some introductory knowledge in machine learning and statistics. Some familiarization with Python programming environments.
(20 reviews)
Recommended experience
Intermediate level
Some introductory knowledge in machine learning and statistics. Some familiarization with Python programming environments.
Import, explore and normalize real world data (HELOC) for evaluating the risk performance of mortgage applications
Train and test a prediction model as a Sequential model based Artificial Neural Network (ANN)
Generate explanations based on profiles of mortgage applicants closest to the individual requesting the explanation.
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Only available on desktop
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations. Since this explanation technique is model agnostic and treats the predictions model as a 'black-box', the guided project can be useful for decision makers within business environments, e.g., loan officers at a bank, and public organizations interested in using trusted machine learning applications for automating, or informing, decision making processes.
The main learning objectives are as follows: Learning objective 1: You will be able to define, train and evaluate an artificial neural network (Sequential model) based classifier by using keras as API for TensorFlow. The pediction model will be trained and tested with the HELOC dataset for approved and rejected mortgage applications. Learning objective 2: You will be able to generate explanations based on similar profiles for a mortgage applicant predicted either as of "Good" or "Bad" risk performance. Learning objective 3: you will be able to generate contrastive explanations based on feature and pertinent negative values, i.e., what an applicant should change in order to turn a "rejected" application to an "approved" one.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Importing and preparing the dataset (9 min)
Training, validating and evaluating the ANN-based prediction model (10 min)
Retrieving similar samples as explanations (9 min)
Retrieving similar samples as explanations II (8 min)
Generate contrastive explanations (10 min)
Some introductory knowledge in machine learning and statistics. Some familiarization with Python programming environments.
The Coursera Project Network is a select group of instructors who have demonstrated expertise in specific tools or skills through their industry experience or academic backgrounds in the topics of their projects. If you're interested in becoming a project instructor and creating Guided Projects to help millions of learners around the world, please apply today at teach.coursera.org.
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.