| 🤝 COLLABORATION |
Learn ML Engineering for Free on ML Zoomcamp
Learn ML engineering for free on ML Zoomcamp and receive a certificate! Join online for practical, hands-on experience with the tech stack and workflows used in production ML. The next cohort of the course starts on September 15, 2025. Here’s what you’ll learn:
Core foundations:
- Python ecosystem: Jupyter, NumPy, Pandas, Matplotlib, Seaborn
- ML frameworks: Scikit-learn, TensorFlow, Keras
Applied projects:
- Supervised learning with CRISP-DM framework
- Classification/regression with evaluation metrics
- Advanced models: decision trees, ensembles, neural nets, CNNs
Production deployment:
- APIs and containers: Flask, Docker, Kubernetes
- Cloud solutions: AWS Lambda, TensorFlow Serving/Lite
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| 📅 Today’s Picks |
Transform PDFs to Pandas with Docling’s Complete Pipeline
Problem:
Most PDF processing tools force you to stitch together multiple solutions – one for extraction, another for parsing, and yet another for chunking.
Each step introduces potential data loss and format incompatibilities, making document processing complex and error-prone.
Solution:
Docling handles the entire workflow from raw PDFs to structured, searchable content in a single solution.
Key features:
- Universal format support for PDF, DOCX, PPTX, HTML, and images
- AI-powered extraction with TableFormer and Vision models
- Direct export to pandas DataFrames, JSON, and Markdown
- RAG-ready output maintains context and structure
| ⭐ Related Post |
Handle Messy Data with RapidFuzz Fuzzy Matching
Problem:
Traditional regex approaches require hours of preprocessing but still break with common data variations like missing spaces, typos, or inconsistent formatting.
Solution:
RapidFuzz eliminates data cleaning overhead with intelligent fuzzy matching.
Key benefits:
- Automatic handling of typos, spacing, and case variations
- Production-ready C++ performance for large datasets
- Full spectrum of fuzzy algorithms in one library
Full Article:
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| ☕️ Weekly Finds |
semantic-kernel
AI Orchestration
Model-agnostic SDK that empowers developers to build, orchestrate, and deploy AI agents and multi-agent systems with enterprise-grade reliability.
transformers
Machine Learning
The model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
whisper
Speech Recognition
Robust Speech Recognition via Large-Scale Weak Supervision. A multitasking model for multilingual speech recognition, translation, and language identification.
