Accredited Expert-Level IBM Watson for Automotive Advanced Video Course

[Martha Thomas]

Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-watson-for-automotive-advanced-video-course Lesson 1: Introduction to IBM Watson for Automotive
1.1 Overview of IBM Watson
1.2 Importance of AI in the Automotive Industry
1.3 Key Components of IBM Watson
1.4 Use Cases in Automotive
1.5 Setting Up Your IBM Watson Environment
1.6 Introduction to Watson Studio
1.7 Introduction to Watson IoT Platform
1.8 Introduction to Watson Assistant
1.9 Introduction to Watson Discovery
1.10 Hands-On: Creating Your First Watson Project

Lesson 2: Data Collection and Preprocessing
2.1 Sources of Automotive Data
2.2 Data Ingestion Techniques
2.3 Data Cleaning and Normalization
2.4 Feature Engineering
2.5 Handling Missing Data
2.6 Data Transformation Techniques
2.7 Introduction to Apache Spark
2.8 Using Watson Data Platform
2.9 Data Storage Solutions
2.10 Hands-On: Preprocessing Automotive Data

Lesson 3: Machine Learning Fundamentals
3.1 Supervised Learning
3.2 Unsupervised Learning
3.3 Reinforcement Learning
3.4 Model Selection and Evaluation
3.5 Overfitting and Underfitting
3.6 Cross-Validation Techniques
3.7 Introduction to Scikit-Learn
3.8 Introduction to TensorFlow
3.9 Introduction to PyTorch
3.10 Hands-On: Building Your First ML Model

Lesson 4: Natural Language Processing (NLP) with Watson
4.1 Introduction to NLP
4.2 Text Classification
4.3 Sentiment Analysis
4.4 Named Entity Recognition (NER)
4.5 Text Summarization
4.6 Chatbot Development
4.7 Watson Natural Language Understanding
4.8 Watson Natural Language Classifier
4.9 Watson Tone Analyzer
4.10 Hands-On: Building an NLP Pipeline

Lesson 5: Computer Vision for Automotive
5.1 Introduction to Computer Vision
5.2 Image Classification
5.3 Object Detection
5.4 Semantic Segmentation
5.5 Image Processing Techniques
5.6 Convolutional Neural Networks (CNNs)
5.7 Transfer Learning
5.8 Watson Visual Recognition
5.9 Integrating Computer Vision with IoT
5.10 Hands-On: Building a Computer Vision Model

Lesson 6: Predictive Maintenance
6.1 Introduction to Predictive Maintenance
6.2 Data Collection for Maintenance
6.3 Feature Selection for Predictive Models
6.4 Time Series Analysis
6.5 Anomaly Detection
6.6 Failure Prediction Models
6.7 Watson Machine Learning
6.8 Integrating Predictive Maintenance with IoT
6.9 Case Studies in Predictive Maintenance
6.10 Hands-On: Developing a Predictive Maintenance System

Lesson 7: Autonomous Vehicles and AI
7.1 Introduction to Autonomous Vehicles
7.2 Sensor Fusion Techniques
7.3 Path Planning and Navigation
7.4 Obstacle Detection and Avoidance
7.5 Real-Time Decision Making
7.6 Simulation and Testing
7.7 Watson IoT for Autonomous Vehicles
7.8 Integrating Watson with Vehicle Systems
7.9 Ethical Considerations in Autonomous Vehicles
7.10 Hands-On: Building an Autonomous Vehicle Simulation

Lesson 8: Customer Experience and Personalization
8.1 Introduction to Customer Experience
8.2 Personalized Recommendations
8.3 Customer Segmentation
8.4 Churn Prediction
8.5 Sentiment Analysis for Customer Feedback
8.6 Watson Assistant for Customer Support
8.7 Watson Discovery for Customer Insights
8.8 Integrating Watson with CRM Systems
8.9 Case Studies in Customer Experience
8.10 Hands-On: Developing a Personalized Customer Experience System

Lesson 9: Supply Chain Optimization
9.1 Introduction to Supply Chain Management
9.2 Demand Forecasting
9.3 Inventory Optimization
9.4 Supplier Risk Management
9.5 Route Optimization
9.6 Real-Time Tracking and Monitoring
9.7 Watson Supply Chain Insights
9.8 Integrating Watson with ERP Systems
9.9 Case Studies in Supply Chain Optimization
9.10 Hands-On: Developing a Supply Chain Optimization Model

Lesson 10: Advanced Analytics and Reporting
10.1 Introduction to Advanced Analytics
10.2 Descriptive Analytics
10.3 Diagnostic Analytics
10.4 Predictive Analytics
10.5 Prescriptive Analytics
10.6 Data Visualization Techniques
10.7 Watson Analytics
10.8 Integrating Watson with BI Tools
10.9 Case Studies in Advanced Analytics
10.10 Hands-On: Creating Advanced Analytics Reports

Lesson 11: Cybersecurity in Automotive
11.1 Introduction to Cybersecurity in Automotive
11.2 Threat Detection and Mitigation
11.3 Intrusion Detection Systems
11.4 Secure Communication Protocols
11.5 Data Encryption Techniques
11.6 Watson for Cybersecurity
11.7 Integrating Watson with Security Systems
11.8 Case Studies in Automotive Cybersecurity
11.9 Ethical Hacking and Penetration Testing
11.10 Hands-On: Developing a Cybersecurity Solution

Lesson 12: Edge Computing for Automotive
12.1 Introduction to Edge Computing
12.2 Benefits of Edge Computing in Automotive
12.3 Edge Device Management
12.4 Real-Time Data Processing
12.5 Edge AI and Machine Learning
12.6 Watson IoT Edge
12.7 Integrating Edge Computing with Cloud
12.8 Case Studies in Edge Computing
12.9 Security Considerations in Edge Computing
12.10 Hands-On: Developing an Edge Computing Solution

Lesson 13: Blockchain for Automotive
13.1 Introduction to Blockchain
13.2 Blockchain in Supply Chain Management
13.3 Vehicle History and Ownership Tracking
13.4 Smart Contracts for Automotive
13.5 Decentralized Applications (DApps)
13.6 Watson Blockchain
13.7 Integrating Blockchain with IoT
13.8 Case Studies in Blockchain for Automotive
13.9 Regulatory Considerations
13.10 Hands-On: Developing a Blockchain Solution

Lesson 14: Human-Machine Interaction
14.1 Introduction to Human-Machine Interaction
14.2 Voice Recognition and Synthesis
14.3 Gesture Recognition
14.4 Augmented Reality (AR) and Virtual Reality (VR)
14.5 User Interface Design for Automotive
14.6 Watson Assistant for HMI
14.7 Integrating HMI with Vehicle Systems
14.8 Case Studies in Human-Machine Interaction
14.9 User Experience (UX) Design
14.10 Hands-On: Developing an HMI Solution

Lesson 15: Fleet Management and Optimization
15.1 Introduction to Fleet Management
15.2 Vehicle Tracking and Monitoring
15.3 Route Planning and Optimization
15.4 Fuel Efficiency and Emission Control
15.5 Predictive Maintenance for Fleets
15.6 Watson IoT for Fleet Management
15.7 Integrating Fleet Management with ERP Systems
15.8 Case Studies in Fleet Management
15.9 Regulatory Compliance
15.10 Hands-On: Developing a Fleet Management Solution

Lesson 16: Advanced Topics in IBM Watson
16.1 Watson Knowledge Studio
16.2 Watson OpenScale
16.3 Watson Studio for Advanced Users
16.4 Watson Machine Learning for Advanced Users
16.5 Watson Discovery for Advanced Users
16.6 Watson Assistant for Advanced Users
16.7 Watson IoT for Advanced Users
16.8 Integrating Watson with Other AI Platforms
16.9 Case Studies in Advanced Watson Applications
16.10 Hands-On: Developing an Advanced Watson Solution

Lesson 17: Ethical and Legal Considerations
17.1 Ethical Considerations in AI
17.2 Bias and Fairness in AI Models
17.3 Privacy and Data Protection
17.4 Regulatory Compliance in Automotive
17.5 Intellectual Property and AI
17.6 Watson for Ethical AI
17.7 Integrating Ethical Considerations in AI Development
17.8 Case Studies in Ethical AI
17.9 Legal Frameworks for AI in Automotive
17.10 Hands-On: Developing an Ethical AI Framework

Lesson 18: Future Trends in Automotive AI
18.1 Emerging Technologies in Automotive AI
18.2 Quantum Computing for Automotive
18.3 5G and Beyond for Automotive
18.4 Autonomous Vehicles Level 5
18.5 Advanced Sensor Technologies
18.6 Watson for Future Automotive AI
18.7 Integrating Future Technologies with Watson
18.8 Case Studies in Future Automotive AI
18.9 Research and Development in Automotive AI
18.10 Hands-On: Exploring Future Automotive AI Technologies

Lesson 19: Project Management for AI Projects
19.1 Introduction to Project Management
19.2 Agile Methodologies for AI Projects
19.3 Scrum and Kanban for AI Projects
19.4 Risk Management in AI Projects
19.5 Stakeholder Management
19.6 Watson Project Management Tools
19.7 Integrating Project Management with AI Development
19.8 Case Studies in AI Project Management
19.9 Best Practices in AI Project Management
19.10 Hands-On: Managing an AI Project

Lesson 20: Capstone Project: End-to-End Automotive AI Solution
20.1 Project Overview and Planning
20.2 Data Collection and Preprocessing
20.3 Model Development and Training
20.4 Model Evaluation and Optimization
20.5 Integration with Vehicle Systems
20.6 Deployment and Scaling
20.7 Monitoring and Maintenance
20.8 Documentation and Reporting
20.9 Presentation and Demonstration
20.10 Feedback and Iteration

Lesson 21: Deep Learning for Automotive Applications
21.1 Introduction to Deep Learning
21.2 Neural Network Architectures
21.3 Convolutional Neural Networks (CNNs)
21.4 Recurrent Neural Networks (RNNs)
21.5 Long Short-Term Memory (LSTM) Networks
21.6 Generative Adversarial Networks (GANs)
21.7 Transfer Learning in Deep Learning
21.8 Watson Studio for Deep Learning
21.9 Integrating Deep Learning with Automotive Systems
21.10 Hands-On: Building a Deep Learning Model for Automotive

Lesson 22: Real-Time Data Processing
22.1 Introduction to Real-Time Data Processing
22.2 Stream Processing Techniques
22.3 Apache Kafka for Real-Time Data
22.4 Apache Flink for Real-Time Data
22.5 Real-Time Analytics with Watson
22.6 Integrating Real-Time Data with IoT
22.7 Case Studies in Real-Time Data Processing
22.8 Performance Optimization for Real-Time Data
22.9 Security Considerations in Real-Time Data Processing
22.10 Hands-On: Developing a Real-Time Data Processing System

Lesson 23: Advanced NLP Techniques
23.1 Advanced Topics in NLP
23.2 Transformer Models
23.3 BERT and Its Variants
23.4 Multilingual NLP
23.5 Sentiment Analysis for Automotive
23.6 Watson Natural Language Understanding for Advanced Users
23.7 Integrating Advanced NLP with Automotive Systems
23.8 Case Studies in Advanced NLP
23.9 Ethical Considerations in NLP
23.10 Hands-On: Building an Advanced NLP Model

Lesson 24: Advanced Computer Vision Techniques
24.1 Advanced Topics in Computer Vision
24.2 Object Tracking and Recognition
24.3 3D Reconstruction
24.4 Pose Estimation
24.5 Image Super-Resolution
24.6 Watson Visual Recognition for Advanced Users
24.7 Integrating Advanced Computer Vision with Automotive Systems
24.8 Case Studies in Advanced Computer Vision
24.9 Performance Optimization for Computer Vision
24.10 Hands-On: Building an Advanced Computer Vision Model

Lesson 25: Advanced Predictive Maintenance
25.1 Advanced Topics in Predictive Maintenance
25.2 Anomaly Detection Techniques
25.3 Failure Mode and Effects Analysis (FMEA)
25.4 Reliability Engineering
25.5 Watson Machine Learning for Advanced Predictive Maintenance
25.6 Integrating Advanced Predictive Maintenance with IoT
25.7 Case Studies in Advanced Predictive Maintenance
25.8 Performance Optimization for Predictive Maintenance
25.9 Ethical Considerations in Predictive Maintenance
25.10 Hands-On: Developing an Advanced Predictive Maintenance System

Lesson 26: Advanced Autonomous Vehicles
26.1 Advanced Topics in Autonomous Vehicles
26.2 Advanced Sensor Fusion Techniques
26.3 Advanced Path Planning and Navigation
26.4 Reinforcement Learning for Autonomous Vehicles
26.5 Simulation and Testing for Autonomous Vehicles
26.6 Watson IoT for Advanced Autonomous Vehicles
26.7 Integrating Advanced Autonomous Vehicles with Vehicle Systems
26.8 Case Studies in Advanced Autonomous Vehicles
26.9 Ethical Considerations in Autonomous Vehicles
26.10 Hands-On: Building an Advanced Autonomous Vehicle Simulation

Lesson 27: Advanced Customer Experience
27.1 Advanced Topics in Customer Experience
27.2 Advanced Personalized Recommendations
27.3 Advanced Customer Segmentation
27.4 Advanced Churn Prediction
27.5 Advanced Sentiment Analysis for Customer Feedback
27.6 Watson Assistant for Advanced Customer Support
27.7 Integrating Advanced Customer Experience with CRM Systems
27.8 Case Studies in Advanced Customer Experience
27.9 Performance Optimization for Customer Experience
27.10 Hands-On: Developing an Advanced Customer Experience System

Lesson 28: Advanced Supply Chain Optimization
28.1 Advanced Topics in Supply Chain Optimization
28.2 Advanced Demand Forecasting
28.3 Advanced Inventory Optimization
28.4 Advanced Supplier Risk Management
28.5 Advanced Route Optimization
28.6 Watson Supply Chain Insights for Advanced Users
28.7 Integrating Advanced Supply Chain Optimization with ERP Systems
28.8 Case Studies in Advanced Supply Chain Optimization
28.9 Performance Optimization for Supply Chain Optimization
28.10 Hands-On: Developing an Advanced Supply Chain Optimization Model

Lesson 29: Advanced Analytics and Reporting
29.1 Advanced Topics in Advanced Analytics
29.2 Advanced Descriptive Analytics
29.3 Advanced Diagnostic Analytics
29.4 Advanced Predictive Analytics
29.5 Advanced Prescriptive Analytics
29.6 Advanced Data Visualization Techniques
29.7 Watson Analytics for Advanced Users
29.8 Integrating Advanced Analytics with BI Tools
29.9 Case Studies in Advanced Analytics
29.10 Hands-On: Creating Advanced Analytics Reports

Lesson 30: Advanced Cybersecurity in Automotive
30.1 Advanced Topics in Cybersecurity in Automotive
30.2 Advanced Threat Detection and Mitigation
30.3 Advanced Intrusion Detection Systems
30.4 Advanced Secure Communication Protocols
30.5 Advanced Data Encryption Techniques
30.6 Watson for Advanced Cybersecurity
30.7 Integrating Advanced Cybersecurity with Security Systems
30.8 Case Studies in Advanced Automotive Cybersecurity
30.9 Ethical Hacking and Penetration Testing for Advanced Users
30.10 Hands-On: Developing an Advanced Cybersecurity Solution

Lesson 31: Advanced Edge Computing for Automotive
31.1 Advanced Topics in Edge Computing
31.2 Advanced Edge Device Management
31.3 Advanced Real-Time Data Processing
31.4 Advanced Edge AI and Machine Learning
31.5 Watson IoT Edge for Advanced Users
31.6 Integrating Advanced Edge Computing with Cloud
31.7 Case Studies in Advanced Edge Computing
31.8 Advanced Security Considerations in Edge Computing
31.9 Performance Optimization for Edge Computing
31.10 Hands-On: Developing an Advanced Edge Computing Solution

Lesson 32: Advanced Blockchain for Automotive
32.1 Advanced Topics in Blockchain
32.2 Advanced Blockchain in Supply Chain Management
32.3 Advanced Vehicle History and Ownership Tracking
32.4 Advanced Smart Contracts for Automotive
32.5 Advanced Decentralized Applications (DApps)
32.6 Watson Blockchain for Advanced Users
32.7 Integrating Advanced Blockchain with IoT
32.8 Case Studies in Advanced Blockchain for Automotive
32.9 Advanced Regulatory Considerations
32.10 Hands-On: Developing an Advanced Blockchain Solution

Lesson 33: Advanced Human-Machine Interaction
33.1 Advanced Topics in Human-Machine Interaction
33.2 Advanced Voice Recognition and Synthesis
33.3 Advanced Gesture Recognition
33.4 Advanced Augmented Reality (AR) and Virtual Reality (VR)
33.5 Advanced User Interface Design for Automotive
33.6 Watson Assistant for Advanced HMI
33.7 Integrating Advanced HMI with Vehicle Systems
33.8 Case Studies in Advanced Human-Machine Interaction
33.9 Advanced User Experience (UX) Design
33.10 Hands-On: Developing an Advanced HMI Solution

Lesson 34: Advanced Fleet Management and Optimization
34.1 Advanced Topics in Fleet Management
34.2 Advanced Vehicle Tracking and Monitoring
34.3 Advanced Route Planning and Optimization
34.4 Advanced Fuel Efficiency and Emission Control
34.5 Advanced Predictive Maintenance for Fleets
34.6 Watson IoT for Advanced Fleet Management
34.7 Integrating Advanced Fleet Management with ERP Systems
34.8 Case Studies in Advanced Fleet Management
34.9 Advanced Regulatory Compliance
34.10 Hands-On: Developing an Advanced Fleet Management Solution

Lesson 35: Advanced Topics in IBM Watson
35.1 Watson Knowledge Studio for Advanced Users
35.2 Watson OpenScale for Advanced Users
35.3 Watson Studio for Advanced Users
35.4 Watson Machine Learning for Advanced Users
35.5 Watson Discovery for Advanced Users
35.6 Watson Assistant for Advanced Users
35.7 Watson IoT for Advanced Users
35.8 Integrating Advanced Watson with Other AI Platforms
35.9 Case Studies in Advanced Watson Applications
35.10 Hands-On: Developing an Advanced Watson Solution

Lesson 36: Advanced Ethical and Legal Considerations
36.1 Advanced Ethical Considerations in AI
36.2 Advanced Bias and Fairness in AI Models
36.3 Advanced Privacy and Data Protection
36.4 Advanced Regulatory Compliance in Automotive
36.5 Advanced Intellectual Property and AI
36.6 Watson for Advanced Ethical AI
36.7 Integrating Advanced Ethical Considerations in AI Development
36.8 Case Studies in Advanced Ethical AI
36.9 Advanced Legal Frameworks for AI in Automotive
36.10 Hands-On: Developing an Advanced Ethical AI Framework

Lesson 37: Advanced Future Trends in Automotive AI
37.1 Advanced Emerging Technologies in Automotive AI
37.2 Advanced Quantum Computing for Automotive
37.3 Advanced 5G and Beyond for Automotive
37.4 Advanced Autonomous Vehicles Level 5
37.5 Advanced Sensor Technologies
37.6 Watson for Advanced Future Automotive AI
37.7 Integrating Advanced Future Technologies with Watson
37.8 Case Studies in Advanced Future Automotive AI
37.9 Advanced Research and Development in Automotive AI
37.10 Hands-On: Exploring Advanced Future Automotive AI Technologies

Lesson 38: Advanced Project Management for AI Projects
38.1 Advanced Project Management Techniques
38.2 Advanced Agile Methodologies for AI Projects
38.3 Advanced Scrum and Kanban for AI Projects
38.4 Advanced Risk Management in AI Projects
38.5 Advanced Stakeholder Management
38.6 Watson Project Management Tools for Advanced Users
38.7 Integrating Advanced Project Management with AI Development
38.8 Case Studies in Advanced AI Project Management
38.9 Advanced Best Practices in AI Project Management
38.10 Hands-On: Managing an Advanced AI Project

Lesson 39: Advanced Capstone Project: End-to-End Automotive AI Solution
39.1 Advanced Project Overview and Planning
39.2 Advanced Data Collection and Preprocessing
39.3 Advanced Model Development and Training
39.4 Advanced Model Evaluation and Optimization
39.5 Advanced Integration with Vehicle Systems
39.6 Advanced Deployment and Scaling
39.7 Advanced Monitoring and Maintenance
39.8 Advanced Documentation and Reporting
39.9 Advanced Presentation and Demonstration
39.10 Advanced Feedback and Iteration

Lesson 40: Final Review and Certification Preparation
40.1 Review of Key Concepts
40.2 Review of Hands-On Projects
40.3 Review of Case Studies
40.4 Review of Best Practices
40.5 Review of Ethical Considerations
40.6 Review of Future Trends
40.7 Preparation for Certification Exam
40.8 Mock Exams and Practice Questions
40.9 Feedback and Q&A Session
40.10 Certification and Next Steps