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Leveraging AI in Financial Services (old)
Preparation
Legal Notices
The Machine Learning Environment
Working in the Sandbox (0:22)
1. Say "Hello" to AI
1.0 About This Course (2:00)
1.1 A Brief History of AI (8:33)
1.2 What is AI, Machine Learning and Deep Learning?
1.3 Machine Learning In Finance
1.4 Checkpoint
1.7 Discussion Points
2. AI Analysis
2.0 How does AI work? (6:32)
2.1 Deploying an AI Solution
2.2 AI, Ethics And The Law
2.3 Explainable AI (XAI)
2.4 AI in Finance
2.5 Case Study, Fidelity International
2.6 Checkpoint
2.7 Discussion Points
3. Big & Small Data and AI
3.0 Learning From Data (4:03)
3.1 Data Formats
3.2 Preparing Data
3.3 Working With Matrices and DataFrames
3.4 Case Study - Amazon Forecasting with DeepAR (0:43)
3.5 Checkpoint
3.6 Discussion Points
4. Applications of Machine Learning
4.0 Introduction to Machine Learning Applications (4:42)
4.1 Natural Language Processing
4.2 Image Processing
4.3 Recommendation Engines
4.4 AI in Robotics and Autonomous Vehicles
4.5 Case Study, Deploying AI as a Scale Service, UBER
4.6 Checkpoint
4.7 Discussion Points
5. Selecting Machine Learning Models
5.0 The Model Selection Process (10:26)
5.1 Regression
5.2 Classification
5.3 Clustering
5.4 Anomaly Detection
5.5 Neural Networks
5.6 Checkpoint
5.7 Discussion Points
6. Applying AI in Finance
6.0 Applying AI in Finance Introduction (3:43)
6.1 AI Across the Customer Lifecycle (1:48)
6.2 Managing Risk, Security, and Underwriting (10:02)
6.3 Identifying Customer Segments (4:23)
6.4 Predicting Values, Optimal Pricing, Rates and Market Movements (5:26)
6.5 Portfolio Development and Fund Management (2:25)
6.6 Checkpoint
6.7 Discussion Points
7. Implementing AI
7.0 Goals, Objectives, Strategy and Tactics (10:27)
7.1 Your AI Journey
7.2 Common Roadblocks
7.3 AI Solution Development Phases
7.4 Capacity Development
7.5 IT Infrastructure for AI
7.6 Building an AI Orientated Culture and Organisation
7.7 Skills for AI
7.8 Checkpoint
7.9 Discussion Points
Appendix - additional live lecture videos
Say 'Hello" to AI (18:27)
AI Analysis (14:14)
Big & Small Data, and Forecasting (17:25)
Natural Language Processing (NLP) (7:18)
Image Processing (4:50)
Recommendation Engines (0:47)
The Model Selection Process (7:19)
Model Accuracy (2:59)
Regression (3:35)
Classification (3:44)
Clustering (1:22)
Anomoly Detection (0:46)
Neural Networks (3:41)
Applying AI in the Customer Lifecycle (4:48)
Credit Scoring (18:53)
Implementation (8:00)
Explainable AI (XAI) - 52 min lecture (51:19)
9. Plan AI Implementation in Your Organisation
9.0 Using the AI Planning Template (0:47)
9.1 Implementation Planning Template
8. Plan a New Financial Product
8.0 Planning Guidance (1:26)
10. Conclusion
10.0 What Did You Learn? (1:12)
Explainable AI (XAI) - 52 min lecture
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