I asked chatGPT about Dave Ebbelaar and here is answer. Based on a thorough review of publicly available information, I’d like to share the following assessment regarding Dave Ebbelaar’s stated background in AI: 🔍 Verified Facts: - Education (2013–2019): Vrije Universiteit Amsterdam→ While the university does offer programs related to data science, no dedicated AI degree existed during this period. It’s likely that AI was covered as part of broader fields like business analytics or data-driven decision-making. - Internship at Ahold Delhaize (2019):→ Focused on marketing mix modeling for Albert Heijn. This is a typical marketing analytics project, with no direct connection to AI development. - Founder of Nouvelle Amsterdam (2021–2022):→ Ran a fashion e-commerce business, including content creation and digital advertising. Again, this aligns more with marketing and entrepreneurship than with AI engineering. - GitHub / public technical outputs:→ Most activity appears only from 2022 onward. There is no clear technical footprint showing AI system development from 2013 onward, as claimed. ✅ Assessment Summary: It seems unlikely that Dave Ebbelaar has 10+ years of deep technical AI experience.He appears to be a capable digital marketer or product creator who likely started working with AI in recent years — perhaps since 2021. The wording on his site appears exaggerated, intended to drive course sales, rather than supported by a documented technical legacy. Maybe Dave explain more..

When building AI agents, you need them to understand your data—whether it’s PDFs, websites, or internal documents. Most tools for this are closed-source, requiring API keys and external platforms. But what if you could do it all in Python with an open-source library? In this week’s video, I show you how to build a fully open-source document extraction pipeline using Docling. You’ll learn how to: - Extract, parse, and chunk documents for AI processing. - Store and retrieve data efficiently with vector databases. - Build a working chat application that can answer questions based on your documents. Watch the video here.

Hi everyone, I've noticed many people in the community seem a bit lost on how to get started with AI, so I decided to write this post in the hope that it might help. There are two types of people I see in this space: those who want to know how to use AI (like using ChatGPT, for example) and those who want to work with AI, meaning they want to create models, develop AI applications, and understand the underlying technology. These are different goals, so it's important to be clear about what you're aiming for first. If you're more interested in how to use AI, such as with tools like ChatGPT, I suggest checking out this resource: Learn Prompting Basics. For those who want to work with AI, whether by building applications or developing models. I recommend starting with an area that interests you. You can always shift focus later once you explore more options. This post also serves as a kind of response to this other one: How to Get Started with AI? Here's my take on a structured yet flexible approach: Start with Python: Python is the primary language for AI. Get comfortable with its basics and libraries like: - NumPy and Pandas for data manipulation. - Matplotlib and Seaborn for data visualization. Learn Key Libraries for AI/ML After mastering Python, focus on: - Scikit-learn for traditional machine learning algorithms. - TensorFlow or PyTorch for deep learning frameworks. Refresh Your Math Skills: - Linear Algebra for neural networks. - Calculus for optimization techniques. - Probability and Statistics for model evaluation. Explore Machine Learning: Key Algorithms: Familiarize yourself with foundational algorithms, including: - Linear Regression: Used for predicting continuous outcomes. - Decision Trees: Useful for classification and regression tasks. - Support Vector Machines (SVM): Effective for high-dimensional spaces and classification tasks. - Clustering Algorithms: Such as K-Means and Hierarchical clustering for grouping data points.

Hey guys, I'm new, I just finished the introduction and the basic, so I would like to unlock the level 3, how to do that please ?

Hello everyone! 👋 I hope this message finds you well. I have an exciting proposition to share with you. Would you be interested in joining a math study group? Do you remember @Alaa Mohammed? She studied physics during her university days and recently came up with the fantastic idea of leading a math-for-machine-learning group. So, we would like to know who would like to explore math together? I'm eager to hear your thoughts on how we can structure our study sessions. Alaa's initial idea is for everyone to enroll in the Coursera course "Mathematics for Machine Learning and Data Science Specialization." Before concerns arise about the course fee (jaja), let me present some options: - You can choose to pay for the course and receive a certificate. - Alternatively, you can apply for financial aid, pay what you can, and commit to completing the course. - If you're on a tight budget, you can audit the course for free, accessing the content without exercises or a certificate. Please let us know if you are interested so we can coordinate and move forward together. If the Coursera course doesn't align with your preferences but you have other great ideas to enhance our math skills, please share them with us. Thank you so much, and Happy Learning! 🤗 @Marco Bottaro @Brandon Phillips @Marcio Pacheco @Grant Fox @Chris Powell-Perez @Aniket Raj and everyone else!