4. Machine Learning for Financial Services (Janani Ravi, 2021)

1. Course Overview: 1. Course Overview 00:00:00 2. Exploring Applications of Machine Learning in Financial Services: 01. Version Check 00:01:59 02. Prerequisites and Course Outline 00:02:15 03. Data and Analytics Trends in Finance 00:04:06 04. Use Cases of ML in Finance - Investment Predictions 00:10:10 05. Use Cases of ML in Finance - Loan Automation 00:14:47 06. Use Cases of ML in Finance - Process Automation 00:17:50 07. Use Cases of ML in Finance - Robo Advisors 00:21:47 08. Use Cases of ML in Finance - Fraud Detection 00:24:41 09. Recurrent Neural Networks for Financial Data 00:27:33 10. Challenges of ML in Finance 00:34:16 3. Case Study - Quantifying Risk and Return of Investment Opportunities: 1. Managing Portfolio Risk 00:40:29 2. Modeling Returns and Risk 00:42:39 3. Stock Correlation Prediction - Background and Context 00:50:04 4. Stock Correlation Prediction - Proposed Hybrid Model 00:54:26 5. Stock Correlation Coefficient Prediction - Methodology a 00:58:23 4. Case Study - Extracting Insights for Fraud Detection: 1. Fraud Detection - Background and Context 01:04:37 2. Fraud Detection - Transaction Features and Customer Features 01:10:36 3. Fraud Detection - Snorkel Labeling 01:12:49 4. Fraud Detection - Methodology and Results 01:16:35 5. Applying Machine Learning Techniques to Financial Data: 1. Classification Use Cases 01:23:32 2. Accuracy Precision and Recall 01:25:05 3. Demo - Fraud Detection - Data Exploration and Preparation Part I 01:29:47 4. Demo - Fraud Detection - Data Exploration and Preparation Part II 01:34:55 5. Demo - Fraud Detection - Classification Models 01:41:25 6. Demo - Fraud Detection - ROC Curves and AUC 01:46:19 7. Summary Resources Used and Further Study 01:50:05