Here's how you can acquire hands-on experience in AI.

While I do see value in some introductory courses - the value of AI to you as an individual is different than the value that I get out of AI. So - have AI teach you, not a generic lesson. Go to GPT, and create a prompt: My name is Sandy, I am very interested in learning about how I can use AI to help me in my current role in doing data analysis to find insights into consumer behavior. I don't even know where to start or what questions to ask. Please put together an interactive training curriculum for me that gets me from knowing nothing to being able to effectively use AI to perform better. Ask me any questions you need to for greater context, review the approach with me, and then when we are ready, teach me everything I need to know.

You can't just read about pottery to be a good potter, you have to get your hands dirty to make good pots. The same stands true for AI too. Online courses can help get practical skills in AI. 👉Online courses often include practical assignments, coding exercises, and real-world projects that simulate industry scenarios. 👉Platforms like Coursera, edX, and Udacity offer specialized AI courses designed by experts from leading institutions. 👉These courses cover essential AI tools and techniques, enabling learners to apply theoretical knowledge in practical settings. 👉Interactive forums and peer reviews enhance the learning experience by fostering collaboration and feedback. So, go ahead & choose a course to acquire hands-on skills.

Online courses not only provide a foundational understanding but also offer specialized tracks tailored for various AI applications, from robotics to natural language processing. Best Practice: Adopt the "15-Minute Rule" — dedicate at least fifteen minutes daily to learning, ensuring consistent progress without overwhelming your schedule.

‣ Leverage online courses as a strategic entry point into AI ‣ Choose platforms offering comprehensive programs, ranging from foundational AI principles to advanced topics ‣ Prioritize courses that integrate theory with practical application through interactive exercises and real-world projects ‣ Utilize the flexibility of online learning to fit AI education into your schedule ‣ Target reputable platforms with high quality content such as Coursera, edX, & Udacity ‣ Opt for courses led by industry leaders and academic experts to gain insights from top-tier professionals. ‣ Engage in hands-on projects that simulate real AI challenges to get practical experience. ‣ Join online AI communities and forums to network with peers to stay updated

For beginners, identify your goals and choose online courses that offer real-world applications and project-based learning. Experiment with SaaS AI platforms like Google Cloud AI or AWS AI through free trials to see AI in action. Intermediate learners should pursue specialized courses and certifications, focusing on advanced topics and industry-relevant projects. Work with open-source frameworks like TensorFlow and PyTorch, and contribute to AI projects on GitHub. Advanced practitioners should stay updated with cutting-edge research, implement novel ideas, and lead strategic AI projects to drive business outcomes. For all levels, leveraging SaaS platforms provides hands-on experience with deploying and optimizing AI solutions.

‣ Master Python programming (simple and with tons of libraries) ‣ Practice coding to master algorithm implementation and data processing. ‣ Start with small projects to build skills and confidence. ‣ Contribute to open-source AI projects to gain practical experience ‣ Develop a portfolio to showcase your skills and projects to potential employers. ‣ Use GitHub to find and contribute to open-source projects. ‣ Practice coding challenges on Kaggle, LeetCode and HackerRank to sharpen problem-solving abilities. ‣ Follow tutorials and project guides to learn best practices and advanced techniques. ‣ Stay updated with AI programming developments through blogs, YouTube videos, etc.

💡💡Code...falter...learn...improve...iterate. That's the mantra to gain hands-on AI skills through regular coding practice. 🧠It helps you understand how algorithms are implemented by allowing you to see the step-by-step process of their creation and optimization. 🧠Working with data through coding also enables you to learn how data is processed, cleaned, and transformed, which is fundamental in AI development. 🧠By regularly tackling coding challenges, you can experiment with different approaches and debug your solutions. 🧠This can help in deepening your comprehension of AI concepts and enhancing problem-solving skills.

To truly excel in AI, you need to be proficient in programming. Languages like Python are widely used in the AI community due to their simplicity and extensive libraries like TensorFlow and PyTorch. Regular coding practice will help you understand how algorithms are implemented and how data is processed. Start by working on small projects, such as building a basic neural network or creating a simple chatbot. Additionally, contributing to open source AI projects can further enhance your skills and help you build a portfolio of your work. This hands-on experience is crucial for mastering AI concepts and demonstrating your capabilities.

Mastering programming is not a must to excel in AI. There is absolutely no need to re-invent the wheel here. Especially against other people who might have better AI infrastructure in place. The focus should shift to how to actually use AI within your own context. People nowadays characterise the AI market as being democratised. State-of-the-art AI is available for everyone to use with a very low cost of entrance. People should leverage this unique situation. Obviously it is still necessary to understand what's happening under the hood. Therefore basic programming knowledge might come in handy. Proficient? Maybe not.

Unless you're at the forefront of developing new AI technology, you don't need a thorough understanding of coding to excel in AI. Most businesses that want to benefit from AI also want to continue using the software they're already using. They plan to benefit from integrations and RAG in conjunction with AI models rather than having new AI models created for them. In these scenarios, understanding of no-code automation tools and how APIs/Webhooks work is more important than coding knowledge.

‣ Understand that data is the lifeblood of AI, essential for building effective models. ‣ Learn to collect, clean, and manipulate data for AI applications. ‣ Gain proficiency in databases and data preprocessing techniques. ‣ Use data visualization tools to present data insights effectively. ‣ Practice with real-world datasets from public repositories to build hands-on experience. ‣ Improve technical skills by analyzing and interpreting complex data. ‣ Develop the ability to derive meaningful insights from data to inform AI models. ‣ Master libraries like Pandas, Numpy, etc., for data handling, Seaborn and Matplotlib, etc. for visualization ‣ Stay updated on best practices for data management and preprocessing.

Effective data handling can significantly enhance the performance of AI systems. Easy-to-Remember Formula: "Garbage In, Garbage Out" — quality of input data determines the quality of the output, stressing the importance of meticulous data cleaning and preparation.

Data is the oxygen that runs the AI algorithms. The decisions and insights made by AI are all based on quality of data you provide. Here are a few steps to learn data handling. 👉Start by collecting relevant datasets from sources like Kaggle or public repositories. 👉Use data cleaning techniques to handle missing values, remove duplicates, and correct errors, ensuring high-quality data. 👉Employ data preprocessing methods such as normalization, encoding categorical variables, and splitting data into training and testing sets. 👉Utilize data visualization tools like Matplotlib and Seaborn to gain insights and identify patterns. 👉Engage with online courses and tutorials to deepen your understanding on data handling.

Data is the lifeblood of AI. To build experience, you must learn how to collect, clean, and manipulate data. This involves understanding databases, data preprocessing techniques, and data visualization tools. Practice by working with real-world datasets available on public repositories like Kaggle or UCI Machine Learning Repository. For example, you can start by cleaning a dataset to remove missing values, normalizing the data, and then using visualization tools like Matplotlib or Seaborn to identify patterns and trends. Analyzing and interpreting data will not only improve your technical skills but also enhance your ability to derive meaningful insights from complex information, making you a more effective AI practitioner.

Here's a refreshing take: Rather than focusing solely on traditional data handling techniques, why not embrace the chaotic beauty of real-world data? Instead of endlessly cleaning data to fit textbook models, dive into messy, unstructured data to uncover hidden patterns. Forget pristine datasets; real-world AI demands the ability to adapt and innovate in the face of imperfection. Learn to thrive amid the noise. This approach doesn't just build technical skills—it fosters resilience, creativity, and a deeper understanding of AI's true potential. In the real world, data isn't just handled; it's wrestled, tamed, and transformed into insights. Give yourself a problem, now solve it with data you find in open resources. Then boast (on LinkedIn)!

Communities often spark collaborations that could lead to breakthrough innovations or start-up opportunities. Consider how diverse perspectives within a community can challenge and refine your own ideas, pushing the boundaries of conventional AI applications.

The tech and AI community is one of the strongest pillars fueling the growth of AI. Imagine it like a virtual team of allies that is always available to support you and help you learn AI better. 👥AI-related communities provide access to a wealth of resources and collaboration opportunities. 👥These communities offer forums, workshops, and hackathons where you can apply theoretical knowledge to practical problems, gaining valuable insights and feedback from peers and experts. 👥Engaging in these activities allows you to stay updated with the latest advancements, share ideas, and receive mentorship. 👥Collaborative projects within these communities fosters innovation and deepens your understanding of AI concepts.

Engaging with a community provides a sense of *belonging* that is vital for human well-being. Sharing experiences, celebrating successes, and tackling challenges, helps us develop a strong tribal connection. In social psychology it's well-known that a sense of belonging has profound effects on mental health (emotional stability, resilience, and overall well-being). Besides, on a neuroscientific level, these feelings of social connection and belonging have been associated with the activation of specific neural networks in the brain (neurotransmitters, such as oxytocin and serotonin - regulating mood and promoting a sense of calm). In many ways, AI communities contribute to the psychological well-being of their members.

The pace of insights and novel combinations of tools is so fast, that being a part of an engaged community is an excellent way to stay at the leading edge. Listen in, jump in, add value where you can and learn. Let's say you're looking to minimize your rag calls by balancing context windows with token costs amongst a team of short-lived agents. There's several ways to tackle it and getting a chance to kick it around with 10 or 50 other people is simply going to get you there quicker. An AI community is where you can go for rapid, clever and creative approaches to new problems. Wonderful resources wherever you can find them.

Para enfrentar os desafios de aquisição e retenção de talentos no campo da IA, considere implementar um "Programa de Intercâmbio de Experiências". Este programa permitiria que profissionais de IA trabalhassem temporariamente em outras empresas parceiras ou startups inovadoras. Essas experiências curtas, de três a seis meses, proporcionariam aos funcionários a oportunidade de aprender novas práticas, explorar diferentes ambientes de trabalho e trazer de volta insights valiosos para sua empresa. Este intercâmbio não só enriquece o conhecimento dos profissionais de IA, como também fortalece as relações interempresariais e promove um ambiente de aprendizado contínuo.

Nothing beats the learning experience gained from building your own AI projects. Start with simple projects that interest you and gradually increase complexity as your skills improve. For instance, begin with creating a basic chatbot using natural language processing, then move on to developing an image recognition system using convolutional neural networks, and eventually design algorithms for predictive analytics. Documenting your projects and reflecting on the challenges you face will deepen your understanding and showcase your problem-solving abilities. This hands-on experience is invaluable for mastering AI and demonstrating your practical expertise to potential employers or collaborators.

A mason learn to create a building by working with bricks rather than just looking at them. In AI, you must start building projects to learn and gain hands-on skills, reinforcing your understanding of key concepts. ✅Start by selecting small, manageable tasks such as creating a basic chatbot, developing a recommendation system, or implementing a simple image classifier. ✅Working on these projects enhances your coding skills, familiarizes you with AI frameworks like TensorFlow and PyTorch, and exposes you to data preprocessing and model training techniques. ✅These projects provide a practical context for troubleshooting and debugging, helping you develop problem-solving skills essential for more complex AI applications.

Projects are a tangible way to demonstrate your skill and creativity in solving real-world problems using AI. Use the "Project Portfolio Pyramid" approach, starting with several small, simple projects at the base and a few complex, large-scale projects at the peak, to showcase a range of skills and depth of knowledge.

Learning by doing is of extremely high importance when learning about AI. However, as you might notice, there are not always relevant projects available to get hands-on experience. Therefore a good tip would be to start-off with some 'fun projects'. Minor problems in your life that could potentially be solved with AI. Or even funny things that you've always wanted to build with AI. Although these projects do not add a lot of value, they still provide you with handy insights that you can use on other (more serious) projects. Make sure however to store the insights. For instance, write digital notes. It isn't about the outcome, it is the process that matters.

Building projects applied to real world problems will be the best work you can do to enhance your learning and be marketable for paid work. You can do it through online courses, open sources communities, meetups, etc. And doing it in a team setting will grow not just your technical skills, but your communication skills.

Generative AI is super accessible right now. Add the OpenAI app to your phone and try to use it at least once a day. You are going to find that the more you use these tools the more problems you think to apply them to. I recently built out an idea I had for a new product and had ChatGPT help me in every step of the journey - defining the vision, creating requirements, and even prototyping with code and image generation. This might not be learning ML in the classic sense but this will get you up the learning curve in harnessing these tools as they become more accessible to the end user.

In a field as dynamic as AI, yesterday's cutting-edge is today's standard. Reflect on how each learning phase not only adds knowledge but also shapes your critical thinking and problem-solving approach, which are invaluable in AI.

Acquiring hands-on experience in AI isn’t just about relentless study and constant updates—it’s about rethinking what ‘learning’ really means. Sure, follow the AI news and read papers, but don’t stop there. Dive headfirst into projects with no clear outcomes, challenge the status quo, and dare to be unconventional. Tinkering, failing, and iterating are your best teachers. Embrace the chaos of trial and error, where real innovation thrives. In AI, it’s the fearless explorers, not just the diligent students, who redefine what’s possible. So, scrap the linear learning path and get your hands dirty in the uncharted territories of AI. Then document what you have achieved (<--very important!). Tell the world about; make it as practical as you can

One thing that helped as a non-programmer is just reading a lot to stay ahead of the game. Then applying what I read into daily work.

Being knowledgeable about AI is not a state, but rather a process. People should continue learning about AI in order to reap the maximum of benefits from it. What helped for me is having one central place (for me a Teams channel) in which you (and others) save everything interesting about AI (active contribution). In addition to that I connected many interesting AI related RSS feeds to the Teams channel (passive contribution). When I have some spare time, I often scroll through this channel to read about the newest developments.

Acquiring hands-on experience in AI involves several steps. Start by learning foundational concepts through online courses, books, or tutorials. Then, practice coding AI algorithms using platforms like TensorFlow or PyTorch. Engage in projects on platforms like Kaggle or GitHub to apply your skills to real-world problems. Collaborate with peers or join AI communities to share knowledge and gain insights. Seek internships or freelance opportunities to work on AI projects in professional settings. Continuously update your skills through workshops, conferences, and online resources. By combining theoretical knowledge with practical experience, you can develop proficiency in AI and prepare for diverse career opportunities.

Integration of ethical considerations in AI development is gaining traction. Regularly engage in ethical dilemmas and scenario analysis to stay ahead of potential ethical challenges in AI applications, preparing you to handle real-world complexities beyond technical solutions.

Testing new generative AI tools and refining prompt engineering are essential for solving practical business problems effectively. Whether for administrative tasks like report generation, technical challenges like software development, or operational needs like supply chain management, experimenting with new tools helps identify the best solutions. Optimizing prompts ensures accurate and useful outputs, enhancing efficiency and productivity. Continuously testing and improving these tools keeps your operations innovative and aligned with business goals, driving better results across all areas.

Personalized Research Agents. Every working professional needs to have their own custom tailored research agents in play. Curating your personal library or knowledge body of information and data around anything that touches your sphere of activity is now simpler and more beneficial than ever before. You'll gain an immediate wider field of view, yet most importantly, this is the fuel for future Agents that you will leverage to increasing effect as new generations of foundation models get released.