What are the best ways to learn about AI for someone new to the industry?

Learn by doing. Spend at least an hour a day interacting with various AIs, ideally while working. The experience will add up quickly. Also, read top newsletters like The Neuron and Ben’s Bites.

My journey into AI started unexpectedly. With a degree in Electrical Engineering, AI wasn't on my radar. But as I learned more, its potential captivated me. My curiosity grew, pulling me towards AI. The urge to solve complex problems and create meaningful solutions drove me further. This passion led to the birth of SensViz, a venture where we could explore AI's power to solve real-world issues. The road wasn't easy, but the desire to make a difference kept me going. For anyone new, finding what excites you about AI could be the spark that fuels your journey into this vibrant field.

🤔 What are the best ways to learn about AI for someone new to the industry? 1. Take a course on the fundamentals on AI. ⏩ Lots of free ones online 2. Follow LinkedIn creators who talk about AI ⏩ Ruben Hassid as an example shares a lot about generative AI 3. Explore AI tools and use them to help you create value in your company. ⏩ ChatGPT, Midjourney, Jasper AI, Notion AI, GitHub's Copilot etc. 👍 Like to support this content

We're still in the early innings of the new AI revolution, so there’s vast opportunities for individuals to make an impact. For beginners I’d recommend: 1. AI Fundamentals: Start with a strong foundation. There are various AI courses / books for specific roles or industry to help you get started 2. Continual learning resources: Stay updated on more frequent AI developments by following AI creators who post regularly. I enjoy newsletters such as Ben’s Bites and the Sequence for both industry and research news. Also check out articles on TechCrunch and VentureBeat. As you learn, reflect on how AI impacts your work, and share your insights with peers. I regularly share my learning insights on LinkedIn, so check out my posts for inspiration.

Focus on the basics first. Before you start learning about advanced AI topics, make sure you have a solid understanding of the basics. Be patient. Learning about AI takes time and effort. Don't get discouraged if you don't understand something right away. Practice regularly. The more you practice, the better you will become at understanding and applying AI concepts. Don't be afraid to ask for help. If you're stuck, don't be afraid to ask for help from others. There are many people who are happy to help you learn about AI.

There's so much material on AI out there that it's often hard to know where to start! It's often helpful to start by looking at how it can potentially apply to your current work role. A great analogy from Cassie Kozyrkov (who's a great resource!) compares these models to microwaves. Not everyone (far from it) needs to know how to build a microwave themselves. Almost everyone, however, can learn to make something with it because someone else already built the appliance. Product/program/project managers for example might be more interested in the business use cases they can use AI models for. People who build the models beneath the hood spend a lot more time developing code and applying mathematical and statistical principles.

You don't have to start with deeply technical skills. There's a lot of learning to do in terms of utilizing AI productively that don't involve coding or data analysis. Start simple and build from there as your skills grow.

Pick a small pilot project and start learning by doing. Look for low hanging fruit and easy wins. Once you have a first win under your belt it will form part of your credentials for your next proposed AI project.

Here's a 30-day roadmap basis my experience: Days 1-3: Learn Python basics Days 4-5: Dive into functions & modules for practical coding. Day 6-7: Work on simple projects, like data parsing scripts. Days 8-10: Study basic statistics and probability. Days 11-12: Learn about algorithms and data structures, focusing on those relevant to AI. Days 13-14: Begin linear algebra, covering vectors and operations. Days 15-17: Explore machine learning concepts. Days 18-19: Get to grips with libraries like scikit-learn for implementing ML models. Days 20-21: Build and evaluate simple models using datasets. Days 22-24: Understand neural networkslike TensorFlow. Days 25-26: Learn about CNNs and RNNs. Days 27-30: Apply all skills on a live project.

AI is a rapidly evolving field, so it's important to stay updated with the latest research and developments. Follow AI news, read research papers, and participate in online forums and communities to connect with fellow enthusiasts and professionals.

It's understandable to feel overwhelmed by the dizzying pace of AI advancement. We're experiencing a technology growing faster than any in history, amplified by hyper-connectivity. Each day brings new capabilities, discoveries and innovations. Hype and fear run rampant on social media, obscuring a clear view. But take heart—while the AI landscape shifts rapidly, the fundamentals will remain stable. Focus on understanding key concepts like machine learning, neural networks and data training. With that base, you can better contextualize the daily changes. No one grasps it all, so be selective in consuming news. Seek trusted sources, diverse opinions, and thoughtful leaders. The basics equip you to navigate an ever-changing AI landscape.

To follow the trends of what is happening in AI, I suggest The Batch newsletter by deep learning.ai , TechCrunch, MIT Technology Review, and Wired.

The real comprehension of AI dawned on me when I started applying what I had learned. I initiated small projects, faced numerous challenges, but with each hurdle, my understanding deepened. At SensViz, we encourage our team to continuously apply their knowledge in real-world scenarios. It's the practical application that unveils the nuances of AI, making you more adept and confident. Every project you undertake, no matter how small, contributes to your grasp of AI. So, don't hesitate to get your hands dirty; it's all part of the learning curve.

A mentor can provide you with guidance, support, and feedback as you learn about AI. A good mentor should be someone who has experience in the field of AI and who is willing to share their knowledge and expertise with you. Here is a list of 10 AI experts at LinkedIn who can be your mentor: 1. Andrew Ng 2. Ian Goodfellow 3. Fei-Fei Li 4. Geoffrey Hinton 5. Yann LeCun 6. Yoshua Bengio 7. Demis Hassabis 8. David Silver 9. Pieter Abbeel 10. Russ Salakhutdinov

The best approach is hands-on experimentation: 🔍 Explore Broadly: Test various AI applications to discover their capabilities firsthand. ✅ Practical Usage: Implement AI in real-life scenarios to gauge what delivers value. ❌ Learn from Failure: Identify what doesn't work to refine your AI strategy. Don't just read about AI—use it, test it, and understand it through experience. #AIExperimentation #TechHandsOn #InnovationLearning

To learn AI as a beginner, it is essential to master the fundamentals of the subject. All the advancements & innovations in AI are based on the core concepts. If you have a solid foundation, you can learn and create anything on top of it. For instance, you can start with the AI 900 - Azure AI Fundamentals. It covers the basic topics very effectively along with the exposure to Microsoft Azure. There are also many YouTube Channels, including mine, that offer hundreds of free learning videos. Once you finish, try to share what you learn with others, such as your peers, friends, etc. It is the best way to validate your learning. Start applying your learning to your projects. Get your hands dirty by diving in. Most importantly, 'Start' learning.

To learn about AI as a newcomer, begin with online courses, textbooks like "Artificial Intelligence: A Modern Approach," and specialized resources on platforms like Coursera, edX, and fast.ai. Join AI-focused online communities and engage with forums like Reddit' Machine Learning. Gain practical experience by working on hands-on projects, using Python and libraries like TensorFlow and PyTorch. Attend webinars, listen to AI podcasts, and stay informed about the latest research through academic papers and journals. Specialize in a particular AI domain, such as NLP or computer vision, and consider enrolling in formal academic programs or attending workshops.

You can read all the articles, listen to all the podcasts, and take all the certifications, but until you start DOING - truly immersing yourself into the systems and using them to assist your day to day activities (both personal and professional)- you won’t learn. Experience creates understanding. And this applies to the consultants and advisory firms as well. If you’re going external for paid help, look for someone who demonstrates their own practice with systems and shows the corresponding results. These are the leaders who will differentiate and move the needle. Here’s to the doers. 👏