Interpretable machine learning applications: Part 3
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.
Buy Now Free
- Level Professional
- المدة 3 ساعات hours
- الطبع بواسطة Coursera
-
Offered by
عن
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.الوحدات
Practical Application via Rhyme
-
1
Readings
-
1
Plugin
-
1
Lab
-
1
Assignment
1
Assignment
- Practice Quiz: Test your Project understanding
1
Labs
- Interpretable/Explainable machine learning applications: Part 3
1
Readings
- Project-based Course Overview
Auto Summary
Discover the intricacies of machine learning with the course "Interpretable Machine Learning Applications: Part 3," offered by Coursera. Designed for intermediate learners in the Data Science & AI domain, this engaging, project-based course spans approximately 50 minutes, making it a concise yet comprehensive educational experience. Under the expert guidance of the instructor, you will delve into advanced explanation techniques and algorithms, specifically tailored to demystify predictions made by complex machine learning models like artificial neural networks. This course is perfect for those looking to deepen their understanding of interpretability in machine learning, and best of all, it comes with a free subscription option. Whether you're a data scientist, AI specialist, or a tech enthusiast, this course will equip you with valuable skills to enhance your machine learning projects.
Epaminondas Kapetanios