#Python Tools for Scientists: An Introduction to Using Anaconda, JupyterLab, and Python's Scientific Libraries →→ Get it at http://
amzn.to/3WrBmZf [744-page book from @NoStarch Press]
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#Coding #DataScience #DataScientist #ComputationalScience #MachineLearning #AI
TOOLS
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Python Tools for Scientists: Anaconda, JupyterLab, Scientific Libraries
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Generative AI on Google Cloud with LangChain and Python
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Generative AI on Google Cloud with #LangChain — Design scalable #GenerativeAI solutions with #Python, LangChain, and Vertex AI on Google Cloud: http://
amzn.to/4frbkPA v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Turn challenges into opportunities by learning advanced techniques -
Data Engineering with Google Cloud Platform: Scalable Data Platform Guide
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#DataEngineering with Google Cloud Platform #GCP — A Guide to leveling up as a #DataEngineer by building a scalable data platform with Google Cloud: http://
amzn.to/4ecMUtM [2nd Edition] v/ @PacktDataML —————
#DataAnalytics #Analytics #CDO #CTO #AI #ML #MLOps #DataScience -
The Data Science Design Manual: Essential Resource for Data Scientists
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The Data Science Design Manual: http://
amzn.to/45lXMTn
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#DataScience #DataScientist #Analytics #AI #ML #MachineLearning #DataViz #DataStorytelling #DataLiteracy -
Machine Learning and Generative AI for Marketing with Python
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#MachineLearning and #GenerativeAI for Marketing using #Python — http://
amzn.to/4dNES9Z via @PacktDataML Key Features: Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques Combine Python programming with the -
Building Production-Ready AI Agents with MLflow Evaluation
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🧱 Agent Bricks makes it easy to build high-quality, production-ready AI agents.
— Databricks (@databricks) 20 septembre 2025
In this demo by Kasey Uhlenhuth, you’ll see how to:
-Build a knowledge assistant for document queries
-Evaluate with MLflow traces + custom metrics
-Improve answers with natural language feedback… pic.twitter.com/aKUWqMgWFCAgent Bricks makes it easy to build high-quality, production-ready AI agents. In this demo by Kasey Uhlenhuth, you’ll see how to: -Build a knowledge assistant for document queries
-Evaluate with MLflow traces + custom metrics
-Improve answers with natural language feedback -
Master Trending AI Tech in 2025 – Complete Learning Roadmap
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Master Trending AI Tech in 2025 – Roadmap Fundamentals (1–1.5 mo) Python | Data Analysis | Math for AI | Git | Cloud Basics Core Machine Learning (1.5–2 mo) Supervised/Unsupervised | Pipelines | Model Monitoring | Feature Eng. Deep
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50+ Free n8n Automation Templates Ready to Copy and Paste
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I compiled 50+ n8n automation templates you can copy & paste into your business or sell to other companies. Just straight plug-and-play systems for: – Lead gen -Content creation – Email outreach – CRM updates – AI workflows – Slack/Discord bots … and more. Follow + Retweet + Reply “YES” and I’ll send it over. This is completely FREE. Don't even want your email.
→ View original post on X — @thatroblennon, 2025-09-20 10:15 UTC
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Grok-4 Fast Model Launch: Cheap, Powerful, 2M Context Window
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@xai was cooking with their new Grok-4 fast Model! So freaking cheap and so good at the same time + 2m context window. This is huts! "2M token context window, and a unified architecture that blends reasoning and non-reasoning modes in one model" Input tokens $0.20 / 1M $0.40 / -
LMArena Benchmarking Gamed by Sycophantic AI Responses
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It is kind of amazing how many good benchmarking tools have been saturated or gamed (whether by accident or on purpose) in the past few months. LMArena really seemed like a good method, but then it turned out that you could just go full syncophantic and people loved it.