Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-watson-studio-advanced-video-course Lesson 1: Introduction to IBM Watson Studio
1.1 Overview of IBM Watson Studio
1.2 Key Features and Benefits
1.3 Setting Up Your Watson Studio Environment
1.4 Navigating the Watson Studio Interface
1.5 Understanding Projects and Assets
1.6 Collaboration Tools in Watson Studio
1.7 Integration with Other IBM Services
1.8 Use Cases and Industry Applications
1.9 Getting Started with Your First Project
1.10 Resources and Documentation
Lesson 2: Data Management in Watson Studio
2.1 Data Sources and Connections
2.2 Importing and Exporting Data
2.3 Data Refinery Overview
2.4 Data Cleaning and Preparation
2.5 Data Transformation Techniques
2.6 Handling Missing Values
2.7 Data Profiling and Visualization
2.8 Data Governance and Security
2.9 Working with Big Data
2.10 Advanced Data Management Techniques
Lesson 3: Machine Learning in Watson Studio
3.1 Introduction to Machine Learning
3.2 Supervised vs. Unsupervised Learning
3.3 Building Your First Machine Learning Model
3.4 Model Training and Evaluation
3.5 Hyperparameter Tuning
3.6 Feature Engineering
3.7 Model Deployment and Monitoring
3.8 Automated Machine Learning (AutoAI)
3.9 Integrating Machine Learning Models with Applications
3.10 Advanced Machine Learning Techniques
Lesson 4: Deep Learning with Watson Studio
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 Long Short-Term Memory (LSTM) Networks
4.6 Transfer Learning
4.7 Building and Training Deep Learning Models
4.8 Evaluating Deep Learning Models
4.9 Deploying Deep Learning Models
4.10 Advanced Deep Learning Techniques
Lesson 5: Natural Language Processing (NLP) with Watson Studio
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 Building NLP Pipelines
5.8 Integrating NLP Models with Applications
5.9 Advanced NLP Techniques
5.10 Use Cases and Industry Applications
Lesson 6: Computer Vision with Watson Studio
6.1 Introduction to Computer Vision
6.2 Image Preprocessing Techniques
6.3 Object Detection and Recognition
6.4 Image Segmentation
6.5 Building Computer Vision Models
6.6 Evaluating Computer Vision Models
6.7 Deploying Computer Vision Models
6.8 Integrating Computer Vision Models with Applications
6.9 Advanced Computer Vision Techniques
6.10 Use Cases and Industry Applications
Lesson 7: Time Series Analysis with Watson Studio
7.1 Introduction to Time Series Analysis
7.2 Time Series Data Preprocessing
7.3 Stationarity and Seasonality
7.4 ARIMA Models
7.5 LSTM for Time Series Forecasting
7.6 Building Time Series Models
7.7 Evaluating Time Series Models
7.8 Deploying Time Series Models
7.9 Advanced Time Series Analysis Techniques
7.10 Use Cases and Industry Applications
Lesson 8: ModelOps and MLOps with Watson Studio
8.1 Introduction to ModelOps and MLOps
8.2 Model Lifecycle Management
8.3 Continuous Integration and Continuous Deployment (CI/CD)
8.4 Model Monitoring and Maintenance
8.5 Automated Model Retraining
8.6 Scaling Machine Learning Models
8.7 Integrating ModelOps with DevOps
8.8 Advanced ModelOps Techniques
8.9 Use Cases and Industry Applications
8.10 Best Practices for ModelOps and MLOps
Lesson 9: Data Visualization with Watson Studio
9.1 Introduction to Data Visualization
9.2 Visualization Tools in Watson Studio
9.3 Creating Interactive Dashboards
9.4 Visualizing Time Series Data
9.5 Visualizing Geospatial Data
9.6 Custom Visualizations
9.7 Integrating Visualizations with Applications
9.8 Advanced Data Visualization Techniques
9.9 Use Cases and Industry Applications
9.10 Best Practices for Data Visualization
Lesson 10: Advanced Data Science Techniques
10.1 Introduction to Advanced Data Science
10.2 Ensemble Learning
10.3 Reinforcement Learning
10.4 Generative Adversarial Networks (GANs)
10.5 Explainable AI (XAI)
10.6 Federated Learning
10.7 Building Advanced Data Science Models
10.8 Evaluating Advanced Data Science Models
10.9 Deploying Advanced Data Science Models
10.10 Use Cases and Industry Applications
Lesson 11: Integrating Watson Studio with IBM Cloud Services
11.1 Overview of IBM Cloud Services
11.2 Integrating with IBM Cloud Object Storage
11.3 Integrating with IBM Cloud Functions
11.4 Integrating with IBM Cloud Databases
11.5 Integrating with IBM Cloud AI Services
11.6 Building End-to-End Solutions
11.7 Advanced Integration Techniques
11.8 Use Cases and Industry Applications
11.9 Best Practices for Integration
11.10 Resources and Documentation
Lesson 12: Security and Compliance in Watson Studio
12.1 Introduction to Security and Compliance
12.2 Data Encryption and Security
12.3 Access Control and Permissions
12.4 Compliance with Regulations (GDPR, HIPAA)
12.5 Auditing and Logging
12.6 Secure Model Deployment
12.7 Advanced Security Techniques
12.8 Use Cases and Industry Applications
12.9 Best Practices for Security and Compliance
12.10 Resources and Documentation
Lesson 13: Collaborative Data Science with Watson Studio
13.1 Introduction to Collaborative Data Science
13.2 Team Collaboration Tools
13.3 Version Control for Data Science Projects
13.4 Sharing and Publishing Models
13.5 Collaborative Data Visualization
13.6 Collaborative Model Training
13.7 Advanced Collaboration Techniques
13.8 Use Cases and Industry Applications
13.9 Best Practices for Collaborative Data Science
13.10 Resources and Documentation
Lesson 14: Scaling Data Science Projects with Watson Studio
14.1 Introduction to Scaling Data Science Projects
14.2 Horizontal and Vertical Scaling
14.3 Distributed Computing
14.4 Scaling Machine Learning Models
14.5 Scaling Data Pipelines
14.6 Advanced Scaling Techniques
14.7 Use Cases and Industry Applications
14.8 Best Practices for Scaling Data Science Projects
14.9 Resources and Documentation
14.10 Case Studies
Lesson 15: Advanced Analytics with Watson Studio
15.1 Introduction to Advanced Analytics
15.2 Predictive Analytics
15.3 Prescriptive Analytics
15.4 Anomaly Detection
15.5 Root Cause Analysis
15.6 Building Advanced Analytics Models
15.7 Evaluating Advanced Analytics Models
15.8 Deploying Advanced Analytics Models
15.9 Advanced Analytics Techniques
15.10 Use Cases and Industry Applications
Lesson 16: Real-Time Data Processing with Watson Studio
16.1 Introduction to Real-Time Data Processing
16.2 Streaming Data Sources
16.3 Real-Time Data Ingestion
16.4 Real-Time Data Analysis
16.5 Building Real-Time Data Pipelines
16.6 Evaluating Real-Time Data Models
16.7 Deploying Real-Time Data Models
16.8 Advanced Real-Time Data Processing Techniques
16.9 Use Cases and Industry Applications
16.10 Best Practices for Real-Time Data Processing
Lesson 17: Edge Computing with Watson Studio
17.1 Introduction to Edge Computing
17.2 Edge Computing Architecture
17.3 Deploying Models to Edge Devices
17.4 Real-Time Edge Analytics
17.5 Edge Device Management
17.6 Advanced Edge Computing Techniques
17.7 Use Cases and Industry Applications
17.8 Best Practices for Edge Computing
17.9 Resources and Documentation
17.10 Case Studies
Lesson 18: Hybrid Cloud Solutions with Watson Studio
18.1 Introduction to Hybrid Cloud Solutions
18.2 Hybrid Cloud Architecture
18.3 Integrating On-Premises and Cloud Environments
18.4 Data Management in Hybrid Cloud
18.5 Deploying Models in Hybrid Cloud
18.6 Advanced Hybrid Cloud Techniques
18.7 Use Cases and Industry Applications
18.8 Best Practices for Hybrid Cloud Solutions
18.9 Resources and Documentation
18.10 Case Studies
Lesson 19: Advanced Data Engineering with Watson Studio
19.1 Introduction to Advanced Data Engineering
19.2 Data Warehousing
19.3 Data Lake Architecture
19.4 ETL and ELT Processes
19.5 Data Pipeline Orchestration
19.6 Advanced Data Engineering Techniques
19.7 Use Cases and Industry Applications
19.8 Best Practices for Advanced Data Engineering
19.9 Resources and Documentation
19.10 Case Studies
Lesson 20: Ethical AI and Bias Mitigation with Watson Studio
20.1 Introduction to Ethical AI
20.2 Bias in Machine Learning Models
20.3 Bias Detection Techniques
20.4 Bias Mitigation Techniques
20.5 Fairness in AI
20.6 Transparency and Accountability
20.7 Advanced Ethical AI Techniques
20.8 Use Cases and Industry Applications
20.9 Best Practices for Ethical AI
20.10 Resources and Documentation
Lesson 21: Advanced Feature Engineering with Watson Studio
21.1 Introduction to Advanced Feature Engineering
21.2 Feature Selection Techniques
21.3 Feature Extraction Techniques
21.4 Dimensionality Reduction
21.5 Feature Scaling and Normalization
21.6 Advanced Feature Engineering Techniques
21.7 Use Cases and Industry Applications
21.8 Best Practices for Advanced Feature Engineering
21.9 Resources and Documentation
21.10 Case Studies
Lesson 22: Advanced Model Evaluation Techniques
22.1 Introduction to Advanced Model Evaluation
22.2 Cross-Validation Techniques
22.3 ROC Curves and AUC
22.4 Precision-Recall Curves
22.5 Confusion Matrix Analysis
22.6 Advanced Model Evaluation Metrics
22.7 Use Cases and Industry Applications
22.8 Best Practices for Advanced Model Evaluation
22.9 Resources and Documentation
22.10 Case Studies
Lesson 23: Advanced Hyperparameter Tuning Techniques
23.1 Introduction to Advanced Hyperparameter Tuning
23.2 Grid Search
23.3 Random Search
23.4 Bayesian Optimization
23.5 Hyperband Method
23.6 Advanced Hyperparameter Tuning Techniques
23.7 Use Cases and Industry Applications
23.8 Best Practices for Advanced Hyperparameter Tuning
23.9 Resources and Documentation
23.10 Case Studies
Lesson 24: Advanced Data Augmentation Techniques
24.1 Introduction to Advanced Data Augmentation
24.2 Image Data Augmentation
24.3 Text Data Augmentation
24.4 Synthetic Data Generation
24.5 Advanced Data Augmentation Techniques
24.6 Use Cases and Industry Applications
24.7 Best Practices for Advanced Data Augmentation
24.8 Resources and Documentation
24.9 Case Studies
24.10 Tools and Libraries
Lesson 25: Advanced Transfer Learning Techniques
25.1 Introduction to Advanced Transfer Learning
25.2 Pre-trained Models
25.3 Fine-Tuning Techniques
25.4 Domain Adaptation
25.5 Advanced Transfer Learning Techniques
25.6 Use Cases and Industry Applications
25.7 Best Practices for Advanced Transfer Learning
25.8 Resources and Documentation
25.9 Case Studies
25.10 Tools and Libraries
Lesson 26: Advanced Reinforcement Learning Techniques
26.1 Introduction to Advanced Reinforcement Learning
26.2 Q-Learning
26.3 Deep Q-Networks (DQN)
26.4 Policy Gradient Methods
26.5 Actor-Critic Methods
26.6 Advanced Reinforcement Learning Techniques
26.7 Use Cases and Industry Applications
26.8 Best Practices for Advanced Reinforcement Learning
26.9 Resources and Documentation
26.10 Case Studies
Lesson 27: Advanced Generative Models
27.1 Introduction to Advanced Generative Models
27.2 Variational Autoencoders (VAEs)
27.3 Generative Adversarial Networks (GANs)
27.4 Autoregressive Models
27.5 Advanced Generative Model Techniques
27.6 Use Cases and Industry Applications
27.7 Best Practices for Advanced Generative Models
27.8 Resources and Documentation
27.9 Case Studies
27.10 Tools and Libraries
Lesson 28: Advanced Explainable AI (XAI) Techniques
28.1 Introduction to Advanced Explainable AI
28.2 LIME (Local Interpretable Model-Agnostic Explanations)
28.3 SHAP (SHapley Additive exPlanations)
28.4 Counterfactual Explanations
28.5 Advanced XAI Techniques
28.6 Use Cases and Industry Applications
28.7 Best Practices for Advanced XAI
28.8 Resources and Documentation
28.9 Case Studies
28.10 Tools and Libraries
Lesson 29: Advanced Federated Learning Techniques
29.1 Introduction to Advanced Federated Learning
29.2 Federated Averaging
29.3 Differential Privacy
29.4 Secure Aggregation
29.5 Advanced Federated Learning Techniques
29.6 Use Cases and Industry Applications
29.7 Best Practices for Advanced Federated Learning
29.8 Resources and Documentation
29.9 Case Studies
29.10 Tools and Libraries
Lesson 30: Advanced Data Governance Techniques
30.1 Introduction to Advanced Data Governance
30.2 Data Lineage
30.3 Data Quality Management
30.4 Data Cataloging
30.5 Advanced Data Governance Techniques
30.6 Use Cases and Industry Applications
30.7 Best Practices for Advanced Data Governance
30.8 Resources and Documentation
30.9 Case Studies
30.10 Tools and Libraries
Lesson 31: Advanced Data Privacy Techniques
31.1 Introduction to Advanced Data Privacy
31.2 Differential Privacy
31.3 Homomorphic Encryption
31.4 Secure Multiparty Computation
31.5 Advanced Data Privacy Techniques
31.6 Use Cases and Industry Applications
31.7 Best Practices for Advanced Data Privacy
31.8 Resources and Documentation
31.9 Case Studies
31.10 Tools and Libraries
Lesson 32: Advanced Data Integration Techniques
32.1 Introduction to Advanced Data Integration
32.2 Data Federation
32.3 Data Virtualization
32.4 Data Mesh Architecture
32.5 Advanced Data Integration Techniques
32.6 Use Cases and Industry Applications
32.7 Best Practices for Advanced Data Integration
32.8 Resources and Documentation
32.9 Case Studies
32.10 Tools and Libraries
Lesson 33: Advanced Data Storage Techniques
33.1 Introduction to Advanced Data Storage
33.2 Object Storage
33.3 Block Storage
33.4 File Storage
33.5 Advanced Data Storage Techniques
33.6 Use Cases and Industry Applications
33.7 Best Practices for Advanced Data Storage
33.8 Resources and Documentation
33.9 Case Studies
33.10 Tools and Libraries
Lesson 34: Advanced Data Compression Techniques
34.1 Introduction to Advanced Data Compression
34.2 Lossless Compression
34.3 Lossy Compression
34.4 Data Deduplication
34.5 Advanced Data Compression Techniques
34.6 Use Cases and Industry Applications
34.7 Best Practices for Advanced Data Compression
34.8 Resources and Documentation
34.9 Case Studies
34.10 Tools and Libraries
Lesson 35: Advanced Data Encryption Techniques
35.1 Introduction to Advanced Data Encryption
35.2 Symmetric Encryption
35.3 Asymmetric Encryption
35.4 End-to-End Encryption
35.5 Advanced Data Encryption Techniques
35.6 Use Cases and Industry Applications
35.7 Best Practices for Advanced Data Encryption
35.8 Resources and Documentation
35.9 Case Studies
35.10 Tools and Libraries
Lesson 36: Advanced Data Anonymization Techniques
36.1 Introduction to Advanced Data Anonymization
36.2 K-Anonymity
36.3 L-Diversity
36.4 T-Closeness
36.5 Advanced Data Anonymization Techniques
36.6 Use Cases and Industry Applications
36.7 Best Practices for Advanced Data Anonymization
36.8 Resources and Documentation
36.9 Case Studies
36.10 Tools and Libraries
Lesson 37: Advanced Data Tokenization Techniques
37.1 Introduction to Advanced Data Tokenization
37.2 Tokenization vs. Encryption
37.3 Data Masking
37.4 Advanced Data Tokenization Techniques
37.5 Use Cases and Industry Applications
37.6 Best Practices for Advanced Data Tokenization
37.7 Resources and Documentation
37.8 Case Studies
37.9 Tools and Libraries
37.10 Compliance and Regulations
Lesson 38: Advanced Data Masking Techniques
38.1 Introduction to Advanced Data Masking
38.2 Static Data Masking
38.3 Dynamic Data Masking
38.4 Advanced Data Masking Techniques
38.5 Use Cases and Industry Applications
38.6 Best Practices for Advanced Data Masking
38.7 Resources and Documentation
38.8 Case Studies
38.9 Tools and Libraries
38.10 Compliance and Regulations
Lesson 39: Advanced Data Lineage Techniques
39.1 Introduction to Advanced Data Lineage
39.2 Data Lineage Tracking
39.3 Data Lineage Visualization
39.4 Advanced Data Lineage Techniques
39.5 Use Cases and Industry Applications
39.6 Best Practices for Advanced Data Lineage
39.7 Resources and Documentation
39.8 Case Studies
39.9 Tools and Libraries
39.10 Compliance and Regulations
Lesson 40: Advanced Data Cataloging Techniques
40.1 Introduction to Advanced Data Cataloging
40.2 Metadata Management
40.3 Data Discovery
40.4 Data Catalog Integration
40.5 Advanced Data Cataloging Techniques
40.6 Use Cases and Industry Applications
40.7 Best Practices for Advanced Data Cataloging
40.8 Resources and Documentation
40.9 Case Studies