AI with Python Cookbook. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/PyCookbook
EDUCATION
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AI with Python Cookbook: BigData Analytics and Machine Learning
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University remains valuable despite AI advancement
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You’re showing why it still makes sense to go to Uni. Really amazing initiative.
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Claude Code Training: Bridging Installation and Productive Mastery
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The demand for proper Claude Code training is real. We're building similar practical content at Towards AI Academy, the gap between "I installed it" and "I'm actually productive with it" is huge.
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Research Workflow Standardization for AI Teams
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Same here! We use skills and md setup with the same docs we give to juniors. The research workflow standardization has been a game changer for us at Towards AI.
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AI Fluency Course for Students: Responsible Use and Collaboration
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→ AI Fluency for Students Si estás estudiando, esto cambia las reglas. > Aprende a usar IA para mejorar tu rendimiento académico
> Planifica tu carrera y colabora con Claude de forma responsable. https://
anthropic.skilljar.com/ai-fluency-for
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Anthropic offers free certified AI academy with agent courses
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Anthropic tiene una academia gratuita con certificados oficiales. 16 cursos.
Desde cero hasta agentes de IA. Te dejo los más interesantes con link directo -
Complete AI Learning Roadmap: Videos, Repos, Books, Papers, Courses
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Stop wasting hours trying to learn AI. 📘📚 I have already done it for you. With one list. Zero confusion. And no fluff 📹 Videos: 1. LLM Introduction: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide – Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents – Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP – Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️
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Data Analyst Roadmap 2026 by Python_Dv
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#DataAnalyst Roadmap 2026
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Being a Software Engineer in 2026
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Being a Software Engineer in 2026: pic.twitter.com/R0BjGI67CF
— Charly Wargnier (@DataChaz) 4 avril 2026Being a Software Engineer in 2026:
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Go Deep Not Wide: AI Interview Success Strategy
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"Don't Be Wide. Go Deep."
— Satya Mallick (@LearnOpenCV) 4 avril 2026
Most people walk into AI interviews trying to prove they know everything. That's exactly what gets them rejected.
Dr. Satya Mallick, CEO of https://t.co/MoLF5sh9ke and https://t.co/iB5io7UA7o, shares the one thing that actually works — go deep, not… pic.twitter.com/472FNslwpw"Don't Be Wide. Go Deep." Most people walk into AI interviews trying to prove they know everything. That's exactly what gets them rejected. Dr. Satya Mallick, CEO of OpenCV.org and BigVision.ai, shares the one thing that actually works — go deep, not wide. The goal isn't to survive the interview. It's to teach your interviewer something they didn't know before. If they walk out thinking "this person knows something I don't" — you've already won. #AIJobs #AIInterview #ComputerVision #MachineLearning #CareerAdvice #TechCareers #OpenCV #DeepLearning #JobInterviewTips
→ View original post on X — @learnopencv, 2026-04-04 06:47 UTC