How do you learn AI and cybersecurity skills?

To excel in AI and cybersecurity, embrace a holistic approach. Blend structured learning with practical experience; participate in Capture The Flag (CTF) challenges to apply cybersecurity concepts in real-world scenarios. For AI, dive into open-source projects, collaborate on GitHub, and experiment with real data. Seek mentorship or join professional associations like (ISC)² for cybersecurity or AI forums on platforms like Kaggle. Stay updated with emerging trends through podcasts like "Security Now" and AI-focused conferences like NeurIPS. Continuous learning, hands-on practice, and networking are your keys to success in these dynamic fields.

If you want to learn any AI then follow below: 1. Fundamentals (Look at basic Mathematics) 2. Learn to write code (At least you should know how to write code in Python) 3. Read research papers and make your own notes wherever required. Look at Paperswithcode. 4. Take a project (look at the problems you want to solve) and start implementing it. 5. Watching videos and doing online courses won't help you much if you don't build a solution. 5. Explore Hugging Face, learn any 1 cloud, and keep exploring new frameworks like Langchain, Llama-index, etc. 6. You should explore Open AI APIs as well.

Generative AI and other foundation models are changing the game in technology. It's more than just a new step—it's a revolution, making powerful AI accessible to everyone. If you're curious about AI and Cybersecurity, don't just stick to online classes. Dive in, pick a cybersecurity problem and, start an undersized project, get hands-on with real-world problems. Pick up a programming language like Python, use AI, ML, and DS libraries, and turn your curiosity into skills. It's not just about learning; it's about doing, creating, and being part of the future.

Don't let the idea that you need to be a math genius deter you from AI and cybersecurity careers. You can enter these fields without extensive math skills. Here's how: 1. Prioritize practical roles like policy development, risk assessment, and system administration. 2. Use AI frameworks like TensorFlow and PyTorch; they handle the math. 3. Collaborate with diverse teams; others can handle math-intensive aspects. 4. Gradually enhance math skills through workshops and online courses. 5. Start in entry-level positions for valuable experience and growth. Remember, your determination and willingness to learn matter just as much. Math may play a role, but it should never be a barrier to your success.

1. Install Reddit and join communities there. 2. Install Telegram and join channels/groups there. 3. Look at the AI Edge Newsletter.

Professionals contemplating a career switch into AI engineering can excel by leveraging their existing domain expertise. Whether they come from fields like law, medicine, creative writing, physics, or education, their deep understanding of these domains is a valuable asset. It enables them to bring a unique perspective to AI projects, offering innovative solutions that draw on their prior experience. This blend of domain knowledge and AI skills positions career switchers as valuable contributors in the rapidly evolving landscape of artificial intelligence.