Bank Loan Approval Prediction With Artificial Neural Nets

4.5
12件の評価
提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

Understand the theory and intuition behind Deep Neural Networks

Build and train a deep learning model using Keras with Tensorflow 2.0 as a backend.

Assess the performance of trained model and ensure its generalization using various Key performance indicators.

Clock2 hours
Beginner初級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

  • Deep Learning
  • Artificial Intelligence (AI)
  • Machine Learning
  • Python Programming
  • classification

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Task 1: Understand the problem statement and business case

  2. Task 2: Import Datasets and Libraries

  3. Task 3: Exploratory Data Analysis

  4. Task 4: Perform Data Visualization

  5. Task 5: Prepare the data to feed the model

  6. Task 6: Understand the theory and intuition behind Artificial Neural Networks

  7. Task 7: Build a simple Multi Layer Neural Network

  8. Task 8: Compile and train a Deep Learning Model

  9. Task 9: Assess the performance of the trained model

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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よくある質問

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