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- Level Foundation
- المدة 3 ساعات hours
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In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc. By the end of this project, you will be able to: - Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry - Understand the theory and intuition behind Deep Neural Networks - Import key Python libraries, dataset, and perform Exploratory Data Analysis. - Perform data visualization using Seaborn. - Standardize the data and split them into train and test datasets. - Build a deep learning model using Keras with Tensorflow 2.0 as a back-end. - Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.Auto Summary
Explore the world of IT & Computer Science with "Bank Loan Approval Prediction With Artificial Neural Nets." Guided by Coursera, this foundational 120-minute course delves into building and training a neural network to predict personal loan approvals using diverse features. You'll gain insights into AI and ML applications in banking, understand deep neural networks, and master data visualization and model assessment with Python, Seaborn, and Tensorflow. Ideal for North American learners, this hands-on project is available with a Starter subscription. Perfect for those eager to enhance their AI expertise in finance.