Visit This Web URL https://masterytrail.com/product/accredited-expert-level-sap-ai-core-services-advanced-video-course Lesson 1: Introduction to SAP AI Core Services
1.1 Overview of SAP AI Core Services
1.2 Importance of AI in Enterprise Solutions
1.3 Key Components of SAP AI Core Services
1.4 Real-World Applications and Use Cases
1.5 Setting Up Your Development Environment
1.6 Introduction to SAP AI Launchpad
1.7 Navigating SAP AI Launchpad
1.8 Understanding SAP AI Models
1.9 Hands-On: Creating Your First AI Model
1.10 Review and Q&A
Lesson 2: Data Preparation and Management
2.1 Data Sources and Types
2.2 Data Cleaning and Preprocessing
2.3 Data Transformation Techniques
2.4 Feature Engineering
2.5 Data Storage Solutions
2.6 Data Integration with SAP Systems
2.7 Data Governance and Compliance
2.8 Hands-On: Data Preparation Exercise
2.9 Advanced Data Management Techniques
2.10 Review and Q&A
Lesson 3: Machine Learning Fundamentals
3.1 Introduction to Machine Learning
3.2 Supervised vs. Unsupervised Learning
3.3 Key Machine Learning Algorithms
3.4 Model Training and Evaluation
3.5 Hyperparameter Tuning
3.6 Overfitting and Underfitting
3.7 Bias-Variance Tradeoff
3.8 Hands-On: Building a Simple ML Model
3.9 Advanced ML Techniques
3.10 Review and Q&A
Lesson 4: Deep Learning with SAP AI
4.1 Introduction to Deep Learning
4.2 Neural Networks and Architectures
4.3 Convolutional Neural Networks (CNNs)
4.4 Recurrent Neural Networks (RNNs)
4.5 Transfer Learning
4.6 Deep Learning Frameworks
4.7 Integrating Deep Learning with SAP AI
4.8 Hands-On: Building a Deep Learning Model
4.9 Advanced Deep Learning Techniques
4.10 Review and Q&A
Lesson 5: Natural Language Processing (NLP)
5.1 Introduction to NLP
5.2 Text Preprocessing Techniques
5.3 Tokenization and Lemmatization
5.4 Sentiment Analysis
5.5 Named Entity Recognition (NER)
5.6 Text Classification
5.7 Chatbots and Conversational AI
5.8 Integrating NLP with SAP AI
5.9 Hands-On: Building an NLP Model
5.10 Review and Q&A
Lesson 6: Computer Vision
6.1 Introduction to Computer Vision
6.2 Image Preprocessing Techniques
6.3 Object Detection and Recognition
6.4 Image Segmentation
6.5 Facial Recognition
6.6 Optical Character Recognition (OCR)
6.7 Integrating Computer Vision with SAP AI
6.8 Hands-On: Building a Computer Vision Model
6.9 Advanced Computer Vision Techniques
6.10 Review and Q&A
Lesson 7: Time Series Analysis
7.1 Introduction to Time Series Analysis
7.2 Time Series Data Characteristics
7.3 Stationarity and Seasonality
7.4 ARIMA Models
7.5 LSTM for Time Series Forecasting
7.6 Anomaly Detection in Time Series
7.7 Integrating Time Series Analysis with SAP AI
7.8 Hands-On: Building a Time Series Model
7.9 Advanced Time Series Techniques
7.10 Review and Q&A
Lesson 8: Reinforcement Learning
8.1 Introduction to Reinforcement Learning
8.2 Markov Decision Processes (MDPs)
8.3 Q-Learning and Deep Q-Networks (DQN)
8.4 Policy Gradient Methods
8.5 Multi-Agent Systems
8.6 Integrating Reinforcement Learning with SAP AI
8.7 Hands-On: Building a Reinforcement Learning Model
8.8 Advanced Reinforcement Learning Techniques
8.9 Real-World Applications
8.10 Review and Q&A
Lesson 9: AI Model Deployment and Monitoring
9.1 Model Deployment Strategies
9.2 Containerization with Docker
9.3 Orchestration with Kubernetes
9.4 Monitoring AI Models in Production
9.5 Performance Metrics and Logging
9.6 Scaling AI Models
9.7 Integrating with SAP Cloud Platform
9.8 Hands-On: Deploying an AI Model
9.9 Advanced Deployment Techniques
9.10 Review and Q&A
Lesson 10: Ethical AI and Bias Mitigation
10.1 Introduction to Ethical AI
10.2 Bias in AI Models
10.3 Fairness, Accountability, and Transparency
10.4 Bias Mitigation Techniques
10.5 Ethical Considerations in AI Development
10.6 Regulatory Compliance
10.7 Integrating Ethical AI with SAP AI
10.8 Hands-On: Bias Mitigation Exercise
10.9 Advanced Ethical AI Techniques
10.10 Review and Q&A
Lesson 11: Advanced Data Visualization
11.1 Introduction to Data Visualization
11.2 Visualization Tools and Libraries
11.3 Creating Interactive Dashboards
11.4 Visualizing Time Series Data
11.5 Visualizing Geospatial Data
11.6 Integrating Visualization with SAP AI
11.7 Hands-On: Building a Data Visualization
11.8 Advanced Visualization Techniques
11.9 Real-World Applications
11.10 Review and Q&A
Lesson 12: AI in Supply Chain Management
12.1 Introduction to Supply Chain AI
12.2 Demand Forecasting
12.3 Inventory Optimization
12.4 Supplier Risk Management
12.5 Predictive Maintenance
12.6 Integrating AI with SAP S/4HANA
12.7 Hands-On: Supply Chain AI Project
12.8 Advanced Supply Chain AI Techniques
12.9 Real-World Applications
12.10 Review and Q&A
Lesson 13: AI in Customer Relationship Management (CRM)
13.1 Introduction to CRM AI
13.2 Customer Segmentation
13.3 Churn Prediction
13.4 Personalized Marketing
13.5 Sentiment Analysis in CRM
13.6 Integrating AI with SAP C/4HANA
13.7 Hands-On: CRM AI Project
13.8 Advanced CRM AI Techniques
13.9 Real-World Applications
13.10 Review and Q&A
Lesson 14: AI in Human Resources (HR)
14.1 Introduction to HR AI
14.2 Candidate Screening and Selection
14.3 Employee Performance Prediction
14.4 Sentiment Analysis in Employee Feedback
14.5 Diversity and Inclusion Analytics
14.6 Integrating AI with SAP SuccessFactors
14.7 Hands-On: HR AI Project
14.8 Advanced HR AI Techniques
14.9 Real-World Applications
14.10 Review and Q&A
Lesson 15: AI in Finance and Accounting
15.1 Introduction to Finance AI
15.2 Fraud Detection
15.3 Credit Scoring
15.4 Financial Forecasting
15.5 Automated Invoice Processing
15.6 Integrating AI with SAP S/4HANA Finance
15.7 Hands-On: Finance AI Project
15.8 Advanced Finance AI Techniques
15.9 Real-World Applications
15.10 Review and Q&A
Lesson 16: AI in Manufacturing
16.1 Introduction to Manufacturing AI
16.2 Predictive Quality Control
16.3 Production Optimization
16.4 Equipment Maintenance
16.5 Integrating AI with SAP Digital Manufacturing
16.6 Hands-On: Manufacturing AI Project
16.7 Advanced Manufacturing AI Techniques
16.8 Real-World Applications
16.9 Ethical Considerations in Manufacturing AI
16.10 Review and Q&A
Lesson 17: AI in Retail
17.1 Introduction to Retail AI
17.2 Inventory Management
17.3 Personalized Recommendations
17.4 Price Optimization
17.5 Customer Behavior Analysis
17.6 Integrating AI with SAP Customer Activity Repository
17.7 Hands-On: Retail AI Project
17.8 Advanced Retail AI Techniques
17.9 Real-World Applications
17.10 Review and Q&A
Lesson 18: AI in Healthcare
18.1 Introduction to Healthcare AI
18.2 Disease Prediction and Diagnosis
18.3 Patient Monitoring and Care
18.4 Drug Discovery and Development
18.5 Integrating AI with SAP for Healthcare
18.6 Hands-On: Healthcare AI Project
18.7 Advanced Healthcare AI Techniques
18.8 Real-World Applications
18.9 Ethical Considerations in Healthcare AI
18.10 Review and Q&A
Lesson 19: AI in Energy and Utilities
19.1 Introduction to Energy AI
19.2 Energy Demand Forecasting
19.3 Predictive Maintenance for Utilities
19.4 Optimizing Energy Distribution
19.5 Integrating AI with SAP for Utilities
19.6 Hands-On: Energy AI Project
19.7 Advanced Energy AI Techniques
19.8 Real-World Applications
19.9 Ethical Considerations in Energy AI
19.10 Review and Q&A
Lesson 20: AI in Public Sector
20.1 Introduction to Public Sector AI
20.2 Citizen Services Optimization
20.3 Fraud and Compliance Management
20.4 Public Safety and Security
20.5 Integrating AI with SAP for Public Sector
20.6 Hands-On: Public Sector AI Project
20.7 Advanced Public Sector AI Techniques
20.8 Real-World Applications
20.9 Ethical Considerations in Public Sector AI
20.10 Review and Q&A
Lesson 21: AI in Transportation and Logistics
21.1 Introduction to Transportation AI
21.2 Route Optimization
21.3 Fleet Management
21.4 Predictive Maintenance for Vehicles
21.5 Integrating AI with SAP Transportation Management
21.6 Hands-On: Transportation AI Project
21.7 Advanced Transportation AI Techniques
21.8 Real-World Applications
21.9 Ethical Considerations in Transportation AI
21.10 Review and Q&A
Lesson 22: AI in Telecommunications
22.1 Introduction to Telecom AI
22.2 Network Optimization
22.3 Customer Churn Prediction
22.4 Fraud Detection in Telecom
22.5 Integrating AI with SAP for Telecommunications
22.6 Hands-On: Telecom AI Project
22.7 Advanced Telecom AI Techniques
22.8 Real-World Applications
22.9 Ethical Considerations in Telecom AI
22.10 Review and Q&A
Lesson 23: AI in Media and Entertainment
23.1 Introduction to Media AI
23.2 Content Recommendation Systems
23.3 Audience Analytics
23.4 Automated Content Generation
23.5 Integrating AI with SAP for Media
23.6 Hands-On: Media AI Project
23.7 Advanced Media AI Techniques
23.8 Real-World Applications
23.9 Ethical Considerations in Media AI
23.10 Review and Q&A
Lesson 24: AI in Real Estate
24.1 Introduction to Real Estate AI
24.2 Property Valuation and Prediction
24.3 Tenant Screening and Management
24.4 Smart Building Management
24.5 Integrating AI with SAP for Real Estate
24.6 Hands-On: Real Estate AI Project
24.7 Advanced Real Estate AI Techniques
24.8 Real-World Applications
24.9 Ethical Considerations in Real Estate AI
24.10 Review and Q&A
Lesson 25: AI in Agriculture
25.1 Introduction to Agriculture AI
25.2 Crop Yield Prediction
25.3 Precision Farming
25.4 Livestock Management
25.5 Integrating AI with SAP for Agriculture
25.6 Hands-On: Agriculture AI Project
25.7 Advanced Agriculture AI Techniques
25.8 Real-World Applications
25.9 Ethical Considerations in Agriculture AI
25.10 Review and Q&A
Lesson 26: AI in Automotive
26.1 Introduction to Automotive AI
26.2 Autonomous Vehicles
26.3 Predictive Maintenance for Vehicles
26.4 Supply Chain Optimization
26.5 Integrating AI with SAP for Automotive
26.6 Hands-On: Automotive AI Project
26.7 Advanced Automotive AI Techniques
26.8 Real-World Applications
26.9 Ethical Considerations in Automotive AI
26.10 Review and Q&A
Lesson 27: AI in Aerospace and Defense
27.1 Introduction to Aerospace AI
27.2 Predictive Maintenance for Aircraft
27.3 Autonomous Systems
27.4 Cybersecurity in Aerospace
27.5 Integrating AI with SAP for Aerospace and Defense
27.6 Hands-On: Aerospace AI Project
27.7 Advanced Aerospace AI Techniques
27.8 Real-World Applications
27.9 Ethical Considerations in Aerospace AI
27.10 Review and Q&A
Lesson 28: AI in Hospitality
28.1 Introduction to Hospitality AI
28.2 Personalized Guest Experiences
28.3 Revenue Management
28.4 Staff Optimization
28.5 Integrating AI with SAP for Hospitality
28.6 Hands-On: Hospitality AI Project
28.7 Advanced Hospitality AI Techniques
28.8 Real-World Applications
28.9 Ethical Considerations in Hospitality AI
28.10 Review and Q&A
Lesson 29: AI in Education
29.1 Introduction to Education AI
29.2 Personalized Learning Paths
29.3 Student Performance Prediction
29.4 Automated Grading Systems
29.5 Integrating AI with SAP for Education
29.6 Hands-On: Education AI Project
29.7 Advanced Education AI Techniques
29.8 Real-World Applications
29.9 Ethical Considerations in Education AI
29.10 Review and Q&A
Lesson 30: AI in Sports
30.1 Introduction to Sports AI
30.2 Player Performance Analysis
30.3 Injury Prediction and Prevention
30.4 Fan Engagement and Experience
30.5 Integrating AI with SAP for Sports
30.6 Hands-On: Sports AI Project
30.7 Advanced Sports AI Techniques
30.8 Real-World Applications
30.9 Ethical Considerations in Sports AI
30.10 Review and Q&A
Lesson 31: AI in Environmental Sustainability
31.1 Introduction to Sustainability AI
31.2 Climate Change Modeling
31.3 Waste Management Optimization
31.4 Energy Efficiency Solutions
31.5 Integrating AI with SAP for Sustainability
31.6 Hands-On: Sustainability AI Project
31.7 Advanced Sustainability AI Techniques
31.8 Real-World Applications
31.9 Ethical Considerations in Sustainability AI
31.10 Review and Q&A
Lesson 32: AI in Cybersecurity
32.1 Introduction to Cybersecurity AI
32.2 Threat Detection and Response
32.3 Anomaly Detection in Networks
32.4 Automated Incident Response
32.5 Integrating AI with SAP for Cybersecurity
32.6 Hands-On: Cybersecurity AI Project
32.7 Advanced Cybersecurity AI Techniques
32.8 Real-World Applications
32.9 Ethical Considerations in Cybersecurity AI
32.10 Review and Q&A
Lesson 33: AI in Robotics
33.1 Introduction to Robotics AI
33.2 Autonomous Robots
33.3 Robotic Process Automation (RPA)
33.4 Human-Robot Interaction
33.5 Integrating AI with SAP for Robotics
33.6 Hands-On: Robotics AI Project
33.7 Advanced Robotics AI Techniques
33.8 Real-World Applications
33.9 Ethical Considerations in Robotics AI
33.10 Review and Q&A
Lesson 34: AI in IoT
34.1 Introduction to IoT AI
34.2 Sensor Data Analysis
34.3 Predictive Maintenance for IoT Devices
34.4 Smart Home and City Solutions
34.5 Integrating AI with SAP for IoT
34.6 Hands-On: IoT AI Project
34.7 Advanced IoT AI Techniques
34.8 Real-World Applications
34.9 Ethical Considerations in IoT AI
34.10 Review and Q&A
Lesson 35: AI in Blockchain
35.1 Introduction to Blockchain AI
35.2 Smart Contracts and AI
35.3 Fraud Detection in Blockchain
35.4 Supply Chain Transparency
35.5 Integrating AI with SAP for Blockchain
35.6 Hands-On: Blockchain AI Project
35.7 Advanced Blockchain AI Techniques
35.8 Real-World Applications
35.9 Ethical Considerations in Blockchain AI
35.10 Review and Q&A
Lesson 36: AI in Augmented Reality (AR) and Virtual Reality (VR)
36.1 Introduction to AR/VR AI
36.2 Enhanced User Experiences
36.3 Training and Simulation
36.4 Integrating AI with SAP for AR/VR
36.5 Hands-On: AR/VR AI Project
36.6 Advanced AR/VR AI Techniques
36.7 Real-World Applications
36.8 Ethical Considerations in AR/VR AI
36.9 Future Trends in AR/VR AI
36.10 Review and Q&A
Lesson 37: AI in Quantum Computing
37.1 Introduction to Quantum AI
37.2 Quantum Machine Learning
37.3 Optimization Problems in Quantum AI
37.4 Integrating AI with SAP for Quantum Computing
37.5 Hands-On: Quantum AI Project
37.6 Advanced Quantum AI Techniques
37.7 Real-World Applications
37.8 Ethical Considerations in Quantum AI
37.9 Future Trends in Quantum AI
37.10 Review and Q&A
Lesson 38: AI in Edge Computing
38.1 Introduction to Edge AI
38.2 Real-Time Data Processing
38.3 Edge Device Management
38.4 Integrating AI with SAP for Edge Computing
38.5 Hands-On: Edge AI Project
38.6 Advanced Edge AI Techniques
38.7 Real-World Applications
38.8 Ethical Considerations in Edge AI
38.9 Future Trends in Edge AI
38.10 Review and Q&A
Lesson 39: AI in 5G Technology
39.1 Introduction to 5G AI
39.2 Network Optimization with AI
39.3 Enhanced Mobile Broadband (eMBB)
39.4 Ultra-Reliable Low-Latency Communication (URLLC)
39.5 Integrating AI with SAP for 5G
39.6 Hands-On: 5G AI Project
39.7 Advanced 5G AI Techniques
39.8 Real-World Applications
39.9 Ethical Considerations in 5G AI
39.10 Review and Q&A
Lesson 40: Future Trends in SAP AI Core Services
40.1 Emerging Technologies in AI
40.2 AI and the Future of Work
40.3 AI Ethics and Governance
40.4 AI Innovations in SAP
40.5 Hands-On: Future AI Project
40.6 Advanced Future AI Techniques
40.7 Real-World Applications
40.8 Ethical Considerations in Future AI
40.9 Preparing for the Future of AI