کتاب صوتی Google Cloud Platform: Natural Language Processing Application Development for Sentiment Analysis at Scale, High Accuracy, and Complexity

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره Google Cloud Platform: Natural Language Processing Application Development for Sentiment Analysis at Scale, High Accuracy, and Complexity

موضوع کلی: برنامه نویسی

موضوع میانی: Google Cloud Platform (GCP)

📋 سرفصل‌های دوره

• 1. Google Cloud Platform: Natural Language Processing Application Development for Sentiment Analysis at Scale, High Accuracy, and Complexity
• 2. Welcome to Google Cloud NLP for Sentiment Analysis
• 3. Course Objectives and Learning Path
• 4. Understanding Natural Language Processing (NLP) Fundamentals
• 5. Introduction to Sentiment Analysis: Concepts and Challenges
• 6. Why Google Cloud Platform (GCP) for Scalable NLP?
• 7. Setting Up Your GCP Project and Environment
• 8. IAM: Managing Permissions and Security on GCP for ML Workloads
• 9. GCP Core Services Overview: Compute, Storage, Networking Essentials
• 10. Introduction to Cloud Storage for Text Data Management
• 11. Organizing and Versioning Large Text Datasets in Cloud Storage
• 12. Python Client Libraries for Interacting with GCP Services
• 13. Fundamentals of Text Preprocessing: Tokenization
• 14. Text Preprocessing: Stemming and Lemmatization Techniques
• 15. Handling Stop Words, Punctuation, and Special Characters in Text
• 16. Advanced Text Normalization and Cleaning Strategies
• 17. Introduction to Feature Extraction for Text Data
• 18. TF-IDF: Term Frequency-Inverse Document Frequency for Text Representation
• 19. Understanding Word Embeddings: Word2Vec Principles
• 20. Advanced Word Embeddings: GloVe and FastText
• 21. Introduction to Contextual Embeddings: BERT Architecture
• 22. Overview of the Cloud Natural Language API
• 23. Performing Sentiment Analysis with the Cloud Natural Language API
• 24. Entity Recognition and Extraction with Cloud Natural Language API
• 25. Syntax Analysis and Parts-of-Speech Tagging with Cloud Natural Language API
• 26. Content Classification and Categorization with Cloud Natural Language API
• 27. Custom Entity Extraction and Annotation with Cloud Natural Language API
• 28. Integrating Cloud Natural Language API into Python Applications
• 29. Batch Processing Large Text Volumes with Cloud Natural Language API
• 30. Real-time Sentiment Analysis using Cloud Natural Language API
• 31. Monitoring and Cost Management for Cloud Natural Language API Usage
• 32. Introduction to Data Ingestion Strategies for NLP on GCP
• 33. Using Cloud Pub/Sub for Real-time Streaming of Text Data
• 34. Building Scalable ETL Pipelines with Apache Beam and Dataflow
• 35. Designing a Dataflow Pipeline for Text Preprocessing and Feature Engineering
• 36. Storing and Querying Processed Text Data in BigQuery
• 37. Introduction to BigQuery ML for Text Analytics and Model Training
• 38. Data Labeling for Custom Sentiment Analysis Models: Best Practices
• 39. Utilizing Vertex AI Data Labeling Service for High-Quality Annotations
• 40. Exporting Labeled Datasets for Model Training on Vertex AI
• 41. Introduction to Vertex AI: The Unified Machine Learning Platform
• 42. Vertex AI Workbench: Managed Notebooks for Development and Experimentation
• 43. Creating and Managing Text Datasets in Vertex AI
• 44. Preparing Text Datasets for Custom Model Training on Vertex AI
• 45. Introduction to Custom Model Training on Vertex AI
• 46. Training a Logistic Regression Model for Sentiment Analysis on Vertex AI
• 47. Model Evaluation Metrics: Precision, Recall, F1-Score, and Accuracy
• 48. Understanding ROC Curves and AUC for Sentiment Classification Models
• 49. Hyperparameter Tuning with Vertex AI Vizier for Optimal Performance
• 50. Custom Training Jobs with TensorFlow on Vertex AI for NLP
• 51. Custom Training Jobs with PyTorch on Vertex AI for NLP
• 52. Introduction to Deep Learning Architectures for Sentiment Analysis
• 53. Recurrent Neural Networks (RNNs) for Sequence Modeling in Text
• 54. Long Short-Term Memory (LSTMs) Networks for Capturing Long-Range Dependencies
• 55. Gated Recurrent Units (GRUs) for Efficient Sequence Processing
• 56. Convolutional Neural Networks (CNNs) for Text Classification
• 57. Understanding Attention Mechanisms in NLP Models
• 58. The Transformers Architecture: Foundation of Modern NLP
• 59. Leveraging Pre-trained BERT Models for Domain-Specific Sentiment Analysis
• 60. Fine-tuning BERT on Custom Sentiment Datasets with Vertex AI
• 61. Other Transformer Models: RoBERTa, XLNet, and ELECTRA
• 62. Advanced Transfer Learning Strategies for High-Accuracy Sentiment Models
• 63. Aspect-Based Sentiment Analysis: Pinpointing Specific Opinions
• 64. Implementing Aspect-Based Sentiment Models on Vertex AI
• 65. Challenges and Solutions for Multilingual Sentiment Analysis
• 66. Building Robust Multilingual Sentiment Models on GCP
• 67. Handling Negation, Sarcasm, and Irony in Sentiment Analysis Systems
• 68. Domain-Specific Sentiment Analysis: Custom Lexicons and Knowledge Bases
• 69. Introduction to MLOps: Principles and Practices for Production ML
• 70. Model Versioning and Lineage Tracking with Vertex AI ML Metadata
• 71. CI/CD for Machine Learning Models with Cloud Build and Vertex AI
• 72. Deploying Custom Sentiment Models to Vertex AI Endpoints
• 73. Batch Prediction for Offline Sentiment Analysis with Vertex AI
• 74. Online Prediction for Real-time Sentiment Analysis with Vertex AI Endpoints
• 75. Optimizing Model Inference: Quantization, Pruning, and Model Compression
• 76. Leveraging TPUs and GPUs for Accelerated Model Training and Inference
• 77. Designing Scalable Architectures for High-Throughput Sentiment Analysis
• 78. Real-time Sentiment Analysis at Scale with Cloud Run and Pub/Sub
• 79. Autoscaling Strategies for Vertex AI Endpoints and Compute Resources
• 80. Monitoring Model Performance, Data Drift, and Concept Drift on Vertex AI
• 81. Detecting and Mitigating Model Bias in Sentiment Analysis Systems
• 82. Introduction to Explainable AI (XAI) for NLP Models
• 83. LIME and SHAP for Interpreting Sentiment Model Predictions
• 84. Ethical Considerations and Responsible AI in NLP and Sentiment Analysis
• 85. Data Security and Privacy Best Practices for Text Data on GCP
• 86. Cost Optimization Strategies for GCP NLP and ML Workloads
• 87. Troubleshooting Common Issues in GCP NLP Application Development
• 88. Advanced Sentiment Analysis with BigQuery ML and Custom Models
• 89. Integrating BigQuery ML with Custom Vertex AI Models for Hybrid Approaches
• 90. Serverless Sentiment Analysis with Cloud Functions for Event-Driven Architectures
• 91. Building a Web Application for Sentiment Analysis with App Engine or Cloud Run
• 92. Using Data Studio for Visualizing Sentiment Trends and Insights
• 93. Dashboarding Sentiment Analysis Results with Looker Studio
• 94. Case Study: Social Media Sentiment Monitoring at Scale
• 95. Case Study: Customer Feedback and Review Analysis for E-commerce
• 96. Case Study: Market Research and Brand Reputation Management
• 97. Best Practices for Developing Production-Ready NLP Applications on GCP
• 98. MLOps Best Practices for Maintaining High-Accuracy Sentiment Models
• 99. Future Trends in NLP, Large Language Models, and Sentiment Analysis
• 100. Advanced Topics: Federated Learning for Privacy-Preserving Sentiment Analysis