You’re interested in AI. How can you get experience in the field?

The evolution of my interest in AI technology began with computer vision and object recognition, gradually expanding into the realms of object detection. This progression led me to explore how to learn from and generalize new data using neural networks and various machine learning techniques. As my journey continued, I became fascinated with the concept of control and Robotics, particularly the application of machine learning methods to system control. The advent of deep learning marked a significant milestone, outperforming many traditional shallow learning and computer vision techniques in numerous use cases. The current challenge I'm facing is developing new models capable of learning effectively from a smaller dataset.

Primeiramente, mergulhei profundamente nos fundamentos da IA, dedicando-me a compreender suas aplica??es, desafios e potencial disruptivo. A chave foi identificar a interse??o entre minha paix?o pela música e tecnologia com as oportunidades emergentes em IA, focando em como essa tecnologia poderia revolucionar a indústria da música, desde a composi??o até a distribui??o digital. A experiência prévia em desenvolvimento de software e consultoria em gest?o empresarial permitiu-me visualizar solu??es inovadoras que a IA poderia oferecer, especialmente em SaaS e tecnologias em nuvem.

In my experience, the best way to find your niche in AI is to start by exploring different areas of the field. There are many different subfields of AI, each with its own unique challenges and applications. Read books, articles, and blog posts about different areas of AI to learn more about them. Once you have a general understanding of the different areas of AI, you can start to narrow down your focus. Think about your interests and skills, and consider how they could be applied to AI. What are you passionate about? What are you good at? Once you have a few areas of AI that you're interested in, start to experiment with different tools and technologies. There are many different AI tools and technologies available, both free and paid.

Why do you like one thing better than another? It's a great mystery in life. But you do like some things better than others. Pay attention—especially to what inspires or calls to you. Then spend more time in the areas that capture your attention. This applies to reading, listening, watching videos, etc. Focus where you are unique and inspired and you'll be more valuable and happier.

Artificial intelligence can be an elusive but exciting technological field. If you’re interested in working on creative, new technology that can impact how people solve everyday problems, the AI field might be right for you. As my experience if one should want to dive into the AI field then he needs to explore online courses, textbooks, and various resources to understand these concepts thoroughly. Engage in coding exercises using languages like Python, familiarize yourself with AI frameworks such as TensorFlow or PyTorch, Transformers and enhance your proficiency through hands-on projects and internships. And the most important thing keep update yourself with the current research and technologies.

Build Project Portfolio (Real World Projects) Build AI Project using LLMs this is a hot topic now. Reach out and try to connect with Senior Professionals in the same field and know about their experience and daily worflow.

Based on your expertise and skills, there are multiple paths you can use to learn AI. For programming and model development, start with Python and familiarize yourself with AI libraries like TensorFlow and PyTorch. For prompt engineering, focus on understanding how to interact effectively with AI models, developing a keen sense for crafting queries that produce desired outcomes. For a deeper understanding of the field's legal and ethical dimensions, study regulations and frameworks guiding AI use, ensuring your projects are socially responsible and compliant. Engage in practical projects, leveraging online resources, and community platforms to apply your skills in real-world scenarios across these diverse areas.

There can be alot of ways to learn and practice AI, however out of all the best way is to learn by doing and building something. The beginners should start by exploring the solutions offered by AI for different sectors and try to recreate them by following a YouTube tutorial. By doing so you will have the sense of achievement, that will be the best time to get in depth and start exploring the details of the techniques and algorithm used. In this manner you will kill two birds with one stone, you will have something to be proud of and can showcase plus you will have acquired the fundamentals along the way.

In my experience, the best way to learn the basics of AI is to start with a general overview of the field. There are many different subfields of AI, each with its own unique challenges and applications. Read books, articles, and blog posts about different areas of AI to learn more about them. Once you have a general understanding of the different areas of AI, you can start to narrow down your focus. Think about your interests and skills, and consider how they could be applied to AI. What are you passionate about? What are you good at? Once you have a subfield of AI that you're interested in, start to learn the basics of programming. Python and R are two popular programming languages that are widely used in AI.

I would like to say get a neural network course (101) and see how a neural network works mathematically. See how weights update in every epoch. Whenever you master the mathematical aspect, go further and get to know the libraries like Tensorflow, Keras, and so on.

The most important part about learning AI is to try things as fast as you can rather than spending too much time on reading books and blog posts. Start with classification, which is a well-established problem in machine learning. Given a piece of text, try to categorise it in categories. This could be anything, ideally you have something from your real life. Build a simple dataset in the format of text:label. Collect 10 examples per case. Save this is a CSV file and look up an AutoML system online. Most cloud providers have one. You should be able to upload the CSV and train a model. Do this and you will learn more in 2 hours than you would by reading 5 ML books.

In Ai field if you want to do deep drive in so do as much practical projects you can, and focus on the problem solving and build new solution which make the people work easy and save there time and cost.

In my experience, the best way to learn AI is by doing. The more you do, the more you'll learn. So don't be afraid to start working on projects. Start small, choose a project that you're interested in, and find a mentor. And don't be afraid to ask for help. Here are a few additional tips: - Use online resources. There are many online resources available to help you learn AI. These include tutorials, courses, and forums. - Attend conferences and meetups. Attending conferences and meetups is a great way to meet other people who are interested in AI. You can learn from them and get feedback on your work. - Contribute to open-source projects. Contributing to open-source projects is a great way to gain experience in AI.

Jupyter notebooks and Google Colab are your new best friends! There's no doubt that doing is the best path to learning and building your skills. But don't make it work. That's why I start with Jupyter-type notebooks. It's easy to play around and have some fun. You'll learn along the way. Pick a topic that seems interesting, then do a web search for "interesting topic" Jupyter Colab. Recreate your own notebook to understand.

Working on real-world AI projects is a fantastic way to gain experience. It allows you to apply your knowledge to solve problems, learn from feedback, and build a strong portfolio. You can find or create projects that interest you, and platforms like GitHub and Medium are great for showcasing your work.

While learning, watching videos, reading articles, etc. ... Who makes you think, "Wow. This person is really cool." Why? Not only will that help you see what areas are best for you to focus, but you can reach out and connect with your people. You'll become more like the people you hang out with.

An example I’ve seen is the value of participating in AI hackathons and project collaborations. These events connect you with other AI enthusiasts and professionals, giving you the chance to work on real-world problems. This not only enhances your coding and problem-solving skills but also helps build a portfolio that can be crucial for career advancement in AI. Also, engaging with AI communities on platforms like GitHub and Stack Overflow has benefited many individuals.

In my experience, the best way to network in AI is to be yourself. Don't try to be someone you're not. Just be genuine, and people will be drawn to you. Also, be willing to help others. This is a great way to build relationships and get your foot in the door. Finally, be patient. Networking takes time. Don't expect to become an overnight success. Just keep networking, and eventually, you will start to see results.

While learning Ai you you can do the practical things as much you can and connect with the other peoples which is related to Ai domain... And Explore more as much you can

The journey to AI expertise isn't a solo mission. Building a network of passionate individuals fuels your learning. Consider attending meetups, hackathons, or conferences to connect with peers, mentors, and experts. Online communities on Slack or Discord offer similar benefits – a constant flow of ideas, questions, and support. By networking, you learn from others, discover new opportunities, and push the boundaries of your AI knowledge.

AI thrives on continuous learning. To stay ahead of the curve, you've outlined fantastic methods: Stay Informed: Devour AI news through blogs, podcasts, and journals. Deepen Your Knowledge: Pursue advanced courses, certifications, or even degrees. Test Yourself: Challenge your skills with competitions on Kaggle or DrivenData. By combining project experience, networking, and staying updated, you'll be well-equipped to navigate the ever-evolving world of AI.

There is always more to learn, and the more you learn, the better you will become at AI. Also, be patient and persistent. Learning AI takes time and effort, but it is worth it in the end. Finally, don't be afraid to experiment and to make mistakes. The best way to learn is by doing, and the best way to learn AI is by doing AI projects.

Ai is improving and updating day by day and there are a lot of new things comes in Ai day by day so keep learning and improving yourself and updated with the latest Ai updates.!

Understanding basic AI concepts like machine learning, deep learning, and AI models is crucial for non-programmers. This knowledge helps individuals grasp the potential and limitations of AI tools and leverage them effectively. Machine learning involves algorithms learning from data to make predictions or decisions, while deep learning focuses on neural networks and complex architectures. Familiarity with AI models such as transformers and LLM (Large Language Models) is essential for understanding advanced AI applications like natural language processing.