Awesome Data Analysis
Section: Resources · Yandex School of Data Analysis course on Natural Language Processing.
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Appears in 4 awesome lists
YSDA course in Natural Language Processing with 2025 materials covering text classification, language models, transformers, and modern NLP techniques. MIT licensed.
Section: Resources · Yandex School of Data Analysis course on Natural Language Processing.
Section: Videos and Online Courses · by Yandex Data School, covering important ideas from text embedding to machine translation including sequence modeling, language models and so on.
Section: 14. Resources & Learning · YSDA course in Natural Language Processing with 2025 materials covering text classification, language models, transformers, and modern NLP techniques. MIT licensed.
Section: Natural Language Processing · 🆓
The definitive curated list of machine learning frameworks, libraries and software organized by language. Covers Python, C++, Java, JavaScript, and more with comprehensive coverage of the ML ecosystem. CC0-1.0 licensed.
Curated list of artificial intelligence courses, books, video lectures, and papers for developers and researchers. MIT licensed.
A ranked list of awesome Python libraries for natural language processing (NLP).
Curated collection of DESIGN.md files representing popular design systems to guide AI coding agents in consistent UI generation. MIT licensed.
Anthropic's official notebook collection covering orchestrator-worker patterns, parallel tool calling, programmatic tool calling (PTC), context compaction, and Agent SDK examples. The patterns/agents/ directory is the reference implementation of every orchestration pattern described in Building…
This course delves into the transformative role of Transformers in deep learning, particularly their impact on the advancement of language models like ChatGPT and GPT-4.
21 lessons covering generative AI fundamentals, prompt engineering, RAG applications, fine-tuning, and LLM app deployment with practical exercises.
10-week, 20-lesson curriculum on data science fundamentals. Covers data preparation, visualization, modeling, and deployment with practical projects.