DeepLearning.AI

Launching today! In "Python for Data Analytics", the third course in the Data Analytics Professional Certificate, you’ll go from writing your first line of code to building real-world analyses using Python, libraries such as pandas, and AI-assisted workflows. You'll learn how to: 📊 Organize and analyze data beyond the limits of spreadsheets 📈 Build clear visualizations and apply statistical tests 📆 Work with time series data to uncover trends and make forecasts 🤖 Use generative AI to write, debug, and explain your code Take this course standalone, or as part of the full certificate! Start learning today: https://hubs.la/Q03gB9rL0

AI agents that can browse the web, fill out forms, and even place online orders are no longer just research demos—they’re being built today. But real-world websites are complex. Layouts change. Popups appear. And one wrong click can cascade into booking the wrong flight or buying the wrong product. In our new course, Building AI Browser Agents, made in collaboration with AGI Inc, you’ll learn how to build web agents and how to make them more reliable using AgentQ, a framework that helps agents self-correct. Guided by instructors Div Garg and Naman Garg, you’ll build agents step-by-step: from scraping and summarizing, to signing up for newsletters, to navigating the open web and choosing optimal actions. 👉 Learn for free: https://hubs.la/Q03hDjb70

Ray Users: Share Your Deep Learning Challenges & Get $200! We're developing tools that reduce deep learning iteration cycles by 5X or more and need input from practitioners actually doing the work. If you're developing AI models in a Ray environment, we'd like to understand your specific technical challenges. This isn't a marketing survey - we need detailed technical feedback to inform our development roadmap. All selected participants will receive a $200 gift card. Limited spots available! Every selected participant will receive a gift card. Fill out the six question form now: https://lnkd.in/ghipN-v2 #deeplearningray, #aideeplearning, #ai, #rayframework

Learning data analytics isn’t something you have to do alone. Join the conversation in our community forum—a space to ask questions, share insights, and connect with others taking the Data Analytics Professional Certificate. Whether you're stuck on a concept or just want to see how others are applying what they’ve learned, this is the place to do it. Jump in: https://hubs.la/Q03hsVnR0 Enroll in the certificate's latest available course, Python for Data Analytics: https://hubs.la/Q03ht03z0

Researchers introduced TabPFN, a transformer model trained on 100 million synthetic datasets to predict unclassified or unregressed spreadsheet or database cells with no fine-tuning required. TabPFN outperformed top decision-tree methods like CatBoost and XGBoost on dozens of benchmarks, setting a new bar for transformer performance in tabular data. Learn more in The Batch: https://hubs.la/Q03hsXyv0

Ready to learn la ciencia de los datos or l'apprentissage automatique? Now you can learn in your language on Coursera like never before! Build career skills by learning in Spanish, French, German, or Portuguese with new translated audio and subtitles in 100+ AI-dubbed courses from IBM, Microsoft, and DeepLearning.AI. https://bit.ly/3GkMkNM

Alibaba launched Qwen2.5-Omni 7B, an open-weights multimodal model with state-of-the-art results in audio-to-text and image-to-text tasks and impressive voice-to-voice performance. Despite its relatively small 7B parameter size, the model rivals or surpasses larger models on several benchmarks. It supports up to 32,768 tokens of text, images, audio, or video input and generates both text and speech outputs. Weights are freely available under the Apache 2.0 license. Learn more in The Batch: https://hubs.la/Q03hs4nK0

Twice a week, Data Points brings you the latest AI news, tools, models, and research in brief. In today’s edition, you’ll find: 🧠 OpenAI launches GPT-4.1 model family 🛠️ New vibe coding tools for Gemini ⚙️ Google’s new TPU is built for agents, inference 🎓 How college students use chatbots Read and subscribe to Data Points: https://hubs.la/Q03hlQgX0

Meta released two open-weight vision-language models, Llama 4 Scout and Llama 4 Maverick, and previewed a third, Llama 4 Behemoth. Built on a mixture-of-experts (MoE) architecture, these models offer greater efficiency by activating only a subset of parameters during inference. 🦙 Scout features an unprecedented 10 million-token context window 🦙 Maverick’s reported benchmarks beat GPT-4o’s 🦙 Behemoth claims to outperform GPT-4.5 and Claude 3.7 Sonnet Learn more in The Batch: https://hubs.la/Q03hcGZz0

🧪 . . . . . Original by /codegino on Reddit.