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AI Agent 从入门到实战：概念理解、MCP 使用、平台实操、工作流自动化
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Building Production NLP Systems with Modern AI: From BERT to LLMs
A practical guide to deploying natural language processing at enterprise scale
Learn how to build, fine-tune, and deploy production-grade NLP systems—from text classification and named entity recognition to semantic search and question answering using modern transformer models.
Deploying AI Computer Vision in Production: From Training to Edge
Building scalable vision AI systems for real-world applications
A practical guide to building and deploying computer vision systems at production scale—covering object detection, image classification, video analytics, and edge deployment strategies.
AI Time Series Forecasting for Business: Demand, Revenue, and Inventory Prediction
Practical machine learning approaches for accurate business forecasting
Master AI-powered time series forecasting for business applications—from demand forecasting and revenue prediction to inventory optimization and anomaly detection using modern deep learning and statistical hybrid models.
Building AI Recommendation Systems for E-Commerce: Beyond Collaborative Filtering
Modern approaches to personalization that drive conversion and retention
Learn how to build and deploy production recommendation systems using modern AI techniques—from two-tower neural networks and session-based recommendations to LLM-powered conversational shopping.
Automating Data Science Workflows with AI: From EDA to Model Deployment
How AutoML and AI assistants are democratizing data science
A comprehensive guide to automating the end-to-end data science workflow using AI tools—from automated exploratory data analysis and feature engineering to model selection, hyperparameter tuning, and production deployment.
Polars for AI Data Processing: Fast DataFrames for ML Pipelines
Polars vs Pandas performance comparison, lazy evaluation, and ML feature engineering
Learn Polars for high-performance data processing in ML pipelines, covering lazy evaluation, lazy query optimization, parallel processing, and integration with ML libraries.
AI-Augmented Data Science: Using LLMs to Accelerate Your Analysis Workflow
Code generation, automated EDA, statistical interpretation, and data storytelling with AI
Learn to integrate AI assistance throughout the data science workflow from exploratory analysis through statistical interpretation, visualization, and stakeholder communication.
Building Efficient Data Labeling Pipelines: Tools, Workflows, and Quality Control
Label Studio, Prodigy, active learning, and human-AI collaboration for annotation
Design efficient data labeling pipelines using Label Studio and Prodigy, implementing active learning to reduce annotation effort, and building quality control systems for training data.
AI Dataset Curation and Quality: Building High-Quality Training Datasets
Data quality frameworks, deduplication, annotation quality control, and data governance
Learn systematic approaches to building high-quality AI training datasets including quality metrics, deduplication strategies, annotation guidelines, inter-annotator agreement, and data governance.
A/B Testing AI Features: Statistical Significance and Practical Significance
Power analysis, sequential testing, and avoiding common pitfalls in AI experiments
Learn rigorous A/B testing methodology for AI features including power analysis, sample size calculation, sequential testing, Bayesian approaches, and avoiding pitfalls like peeking and p-hacking.