Build real intuition through colourful, hands-on puzzles instead of boring theory. It helps you see what’s happening under the hood, which means better debugging, stronger model intuition, and sharper fundamentals.
Linear algebra, but actually fun.
#math#ml#data#DataScience
🚀 LLM agents too slow and unpredictable? Join Ben Auffarth on Oct 30 to build low-latency, reliable AI workflows on CPUs using ModernBERT, conformal prediction & LangGraph. No GPUs needed! https://t.co/LKq01gLCJb Save 35% with NL35 | Register now!
@benji1a 🤖 Take RAG from demo to production with hybrid retrieval, reranking, RAGAS, benchmarking, self-checking, and guardrails. Build with Ben on Aug 29 and save 45% with OMEM45. https://t.co/F5oQvKx4jc
🤖 Take RAG from demo to production with hybrid retrieval, reranking, RAGAS, benchmarking, self-checking, and guardrails. Build with Ben on Aug 29 and save 45% with OMEM45. https://t.co/F5oQvKx4jc
Your #RAG demo just works but does it survive production?
4-hour hands-on build lab, Sat 29 Aug: production RAG on small open models. Hybrid retrieval, guardrails, benchmarks + cost.
👉 https://t.co/s2L79rOdX7
@packtdataml
✨Community Giveaway, sponsored by @PacktPublishing@PacktDataML 🚀
Win 3 bestselling Python books from Packt Publishing — free entry, 5 winners, each receives the full set in both PDF and ePub formats: https://t.co/UL4o1Kxp8a
1) Python Illustrated — a visual, beginner-friendly intro to Python
2) Learn Model Context Protocol with Python — build agentic AI systems with MCP
3) Python Machine Learning By Example (4th Edition) — practical Machine Learning workflows
Bonus: if entries reach 1,500 by July 31, the prize pool doubles to 10 winners.
—————
#DataScience #MachineLearning #DataScientist #Coding #ML #AI
✨Community Giveaway, sponsored by @PacktPublishing@PacktDataML 🚀
Win 3 bestselling Python books from Packt Publishing — free entry, 5 winners, each receives the full set in both PDF and ePub formats: https://t.co/T3DB4XavG7
1) Python Illustrated — a visual, beginner-friendly intro to Python
2) Learn Model Context Protocol with Python — build agentic AI systems with MCP
3) Python Machine Learning By Example (4th Edition) — practical Machine Learning workflows
Bonus: if entries reach 1,500 by July 31, the prize pool doubles to 10 winners.
—————
#DataScience #MachineLearning #DataScientist #Coding #ML #AI
Stop chasing better prompts. Start building better data pipelines. Join Nikola Ilic's hands-on LLM Data Engineering workshop and save 35% with DATAPRO35 👉https://t.co/KP9oYMafwp #GenAI#RAG#Python
The future of GenAI isn't better prompts—it's better workflows. Learn how production-ready AI combines RAG, Python, APIs, and guardrails to solve real problems. 👇https://t.co/NhyzXbO6sB
Tomorrow: Graph RAG workshop.
Build a knowledge graph chatbot from scratch. Live. With code you keep.
A few seats still open.
🔗 https://t.co/u02PZzrR9q
See you Saturday.
4 days until my Graph RAG workshop.
Here's what you build in 3.5 hours:
→ Entity extractor from raw text
→ Coreference resolver
→ Relation extraction pipeline
→ Knowledge graph in NetworkX
→ Grounded LLM chatbot on top
Certificate included. Last chance at 40% off
Most corporate AI ethics talks just dodge accountability. AI leader Marijn Markus cuts through the hype to expose the reality of hallucinations and broken data infrastructure.
Read it here: https://t.co/z6qwrGm3fT
If your RAG struggles with multi-hop reasoning, corpus-wide insights, or explainability, it's time to look beyond vector search. 📖 [https://t.co/KAddXNnuUK] #GraphRAG#DataScience
🚀 Want to build LLM applications that are actually reliable in production?
Join our hands on virtual workshop to master DSPy, automated prompt optimization, evaluation workflows, and production ready LLM engineering.
Buy now: https://t.co/cOnq8ZdI2U
RAG gives AI access to the right knowledge, while CAG gives AI the right context. As enterprise AI evolves, success won't depend on what models know, but on how well they understand users, workflows, and decisions
#rag#machinelearning
Multi-hop question: "What did the CEO of Company X's subsidiary do in 2019?"
Vector RAG: retrieves chunks about Company X. Struggles with the chain.
Knowledge graph: follows the ownership edge, finds the subsidiary, finds the CEO, finds the 2019 event.
From raw documents to production-ready RAG systems - master the engineering choices behind successful LLM applications.
Register now and claim your Early bird discount
Save your spot: https://t.co/qTd7AuBxHO
🤖 One of our peer teams at Packt is hosting a giveaway featuring three AI books. Thought it might be of interest to this community. 👉 Packt Monthly Giveaways - https://t.co/inBdxdVLFP
Move beyond traditional RAG and learn how to build AI applications that understand relationships, not just documents. Join this hands-on workshop and create a production-ready Graph RAG system from scratch.
Register here: https://t.co/qU5P36JNA2
#datascience#rag#data#tech