Відео

Дякую ☺️

Thank you (: 🙏

you are always welcome :)

You're welcome 😊

Sure (: 👌

Then Go and fight with army, don't sit and comment .

Thank you 🙏

Elon said about free speech on Twitter/X with him. But forgot to mention, that it will be not for all, but just to his friends. Classic... 😅

People can be manipulated by themselves, but AI, social media mess , informational manipulation play a crazy and dark joke with people’s brains and mindset. So, critical thinking skills are on the top list right now.

Agree (:

What about me? 😅

Thank you for joining me today (: and for your comment as well :) nice to have you on my board!

Hey, Vince! Here's something I’ve prepared for you : ### 1. **Learn Python** Python is the most popular programming language in the AI and ML communities due to its simplicity and the vast array of libraries available. Start with basic Python programming to get comfortable with syntax and basic programming concepts like loops, conditional statements, functions, and data structures. - **Resources:** Codecademy, Coursera (Python for Everybody), and the official Python documentation for tutorials. ### 2. **Understand Data Manipulation and Analysis** Before diving into ML, you should be comfortable with data manipulation and analysis, as ML heavily relies on data. - **Libraries to Learn:** Pandas for data manipulation, NumPy for numerical data processing, and Matplotlib/Seaborn for data visualization. - **Resources:** Kaggle offers hands-on Python courses focused on data manipulation and analysis. ### 3. **Learn the Basics of Machine Learning** Start with the fundamentals of ML, understanding different types of learning (supervised, unsupervised, reinforcement learning) and basic algorithms. - **Resources:** Coursera (Andrew Ng’s Machine Learning course is highly recommended), edX, and Fast.ai for practical, hands-on courses. ### 4. **Dive Into Deep Learning** After grasping the basics of ML, you can start exploring deep learning, which is a subset of ML that uses neural networks with many layers. - **Libraries to Learn:** TensorFlow and PyTorch are the most popular libraries. - **Resources:** Fast.ai offers practical deep learning courses. DeepLearning.ai (also by Andrew Ng) on Coursera provides comprehensive deep learning specialization. ### 5. **Build Projects** The best way to learn is by doing. Start with small projects and gradually increase complexity. Kaggle competitions are a great way to practice and learn from others. ### 6. **Read and Stay Updated** AI and ML fields evolve rapidly. Stay updated by reading research papers, blogs, and books. Follow AI researchers and practitioners on social media and platforms like Medium, Towards Data Science, and ArXiv for the latest research papers. ### 7. **Join a Community** Join AI and ML communities online (such as Reddit, Stack Overflow, and GitHub) . Good luck, Vince!

@@Nadia.genaipro ah thanks you so much you're an angel from above this is so helpful ☺️

@@Vince_86 I’m always here and happy to help 🫡

Thank you 🙏 😊

Agree, should work on it (: thank you for your feedback.

Graziaz, my friend 😊