Here's how you can start a career in the field of AI without any background.

Deeplearning.ai offers some great beginner-level courses on the fundamentals of artificial intelligence. When I started my AI journey in 2019, I began with Python and deep neural networks. However, were I to start today, I would first focus on learning how LLMs work, as well as LangChain and Prompt Engineering, even if you don't have a dev background, there are a ton of no-code and low-code tools available. These basics have been extremely helpful in my work as an AI consultant and developer.

The best way is to build as you learn the fundamentals. Think of applications, use cases, problems you have around and go deeper and ask questions. There is no point(except jobs) in learning everything and just talking about it. If you solve a problem you may design some new techniques or algorithms which have never been done before.

Step 1: Open an OpenAI API account Step 2: Open a FlutterFlow account Step 3: Follow a Youtube tutorial for linking your OpenAI API into a FlutterFlow app project Step 4: Make something interesting Building with Generative AI is ludicrously easy from a software development perspective. I would say jump in and try to make something. If you just follow some tutorials you can be making a useful AI app in under a day. As an example, you can take your contries constitution or other legal works, load it into the OpenAI API, link this to a FlutterFlow App and now you have a 'Constitution AI' that people can go 'is this legal?' etc.

In this fast-paced world of technology, I believe that starting a career in AI does not necessarily require an AI background. Knowledge that is relevant today may become obsolete in the next three months. Therefore, it is crucial to stay informed, upgrade our skills, learn new things, and adapt to the rapid pace of technological progress. In this dynamic landscape, flexibility and a growth mindset are paramount. Embracing change, being open to new challenges, and cultivating a passion for continuous learning will be the keys to thriving in the ever-evolving industry. As we navigate this era of innovation, let us remember that the journey is just as important as the destination.

?? Begin with Core AI Knowledge Entering AI can seem like a maze without a map. Kickstarting your journey with foundational courses, particularly through platforms like deeplearning.ai, offers structured guidance, making complex concepts approachable and laying down a solid groundwork for specialized areas within AI. This focused start is crucial for building a resilient and adaptable AI career.

? Gain a solid foundation in probability, statistics, linear algebra, and calculus ? Invest your time in reputed courses (deepleanring.ai, or universities that provides a certification on AI ) ? Learn supervised, unsupervised, semi-supervised, and reinforcement learning ? Explore popular ML algorithms and deep learning architectures like CNNs, RNNs, LSTMs, GANs ? Practice with AI/ML libraries and frameworks like scikit-learn, TensorFlow, PyTorch, Keras ? Work on real-world datasets and participate in AI communities, forums, and hackathons ? Understand AI ethics and the societal implications of AI systems ? Gain practical experience through internships or projects in AI companies or research labs

It's important to choose your field in this vast AI landscape. Once you've chosen your field, dive in. You can buy online courses or watch YouTube videos that talk about NLP, machine learning or more, read related articles or research papers .

Delving into AI-specific knowledge is crucial after mastering programming skills. Understanding machine learning, neural networks, natural language processing, and robotics are essential for a well-rounded AI education. Online courses and MOOCs offer valuable resources for deepening your expertise in these areas. Continuous learning and exploration in AI will not only enhance your skills but also contribute to the advancement of this rapidly evolving field. Keep exploring and expanding your knowledge in AI to stay ahead in the game.

? Begin with simple projects like building a basic recommendation system to apply fundamental AI concepts ? As you gain more experience, take on more challenging projects (eg: implementing machine learning models or integrating AI with apps) ??Contribute to open-source projects on platforms like GitHub to learn professional coding practices & collaborative dev. ??Document and showcase your work by maintaining an online portfolio ?? Use project-based learning platforms like Udacity to learn by doing and receive feedback from experts ???Participate in hackathons & coding competitions to develop practical AI solutions. ?Share your AI projects on professional networks and engage in discussions to enhance your understanding & visibility.

??? Dive Into DIY AI Projects Jumping straight into building projects, from simple chatbots to more complex systems, enhances your practical skills and showcases your prowess beyond theoretical knowledge, making you stand out in the AI field.

Now that you've covered the basics, start doing. Remember, the first 20 hours of practice can give you the steepest learning curve. I suggest using AI to potentially ease your daily struggles, by that you'll automatically try to improve the AI when it’s not doing that great of a job, which will mainly be the case. with that, you keep practicing and quickly improve over time. A good start for me was to make an fine-tune and integrate an AI model that knows everything about me, and talks like me. It's a fun project, and it allows you to easily monitor the AI's performance. After you've successfully done this, you can start thinking of what else you can make. Then start thinking of the daily struggles, and what the AI should be able to do.

To apply your learning in the field of Artificial Intelligence, it is essential to get your hands dirty by working on real-world projects. You can begin with simple exercises and gradually move to more challenging ones to hone your skills. To practice and showcase your abilities, you can utilize platforms like Kaggle, which provides a plethora of datasets and competitions to work on. By participating in such competitions, you can learn from the community, gain exposure, and demonstrate your proficiency in AI.

I suggest you : Hands-on Practice: Kaggle offers a wealth of machine learning competitions and datasets to practice your skills. GitHub also has numerous open-source AI projects you can contribute to. or Personal Projects: Think of an AI application that interests you and build a project around it. This demonstrates your initiative and helps you showcase your abilities.

LinkedIn can be an excellent resource for networking as well. You can find, follow and connect with experts across all things AI. There are individuals, groups and companies you can follow. You can subscribe to newsletters. You can join live events. A theme you will typically find in innovative technologies such as a AI, is that there are many innovators and early adopters highly engaged in learning, designing, delivering and using these technologies. These types tend to be great at learning, passionate about the technology and enjoy talking with and mentoring others. I enjoy opportunities pretty much every day to learn from others here on LinkedIn and share my knowledge and expertise. It has been a very positive experience for me.

Networking extensively is indispensable when venturing into AI without prior experience. Personally, I've found that joining online communities like LinkedIn groups dedicated to AI or participating in AI-focused forums helped me connect with professionals in the field. Actively engaging in discussions, sharing insights, and seeking advice from experienced individuals not only broadens your knowledge but also opens doors to potential opportunities. Attending industry events such as AI conferences and workshops provides a platform to establish meaningful connections and gain exposure to the latest advancements. Building a diverse network in the AI community can significantly accelerate your career growth and enhance your learning journey.

Attending AI events in my local area and networking with other enthusiastic and energized individuals who are passionate about AI has been invaluable. I have found everyone i've met extremely eager to share their knowledge and experiences and collaborate on ideas. A good place to start is to check out what's happening at local universities or startup hubs. Most of them are free!

?? Expand Your Circle Starting from scratch in AI can feel daunting, but the key lies in whom you know as much as what you know. Engaging actively within AI communities, both online and offline, can open doors to invaluable insights, mentorship, and opportunities otherwise hidden on your solo journey, setting a solid foundation for a thriving career in AI.

don't look far. linkedin is the best option. you can network to engage with other experts to learn even faster. Don't be afraid to ask for appointments with them to discuss relevant questions and specific topics. you can also network with learner like you through forum. This is really necessary for you!

Keep learning. Here are some considerations. How do we keep the humanity focus at the forefront? Ponder over this then write about it. Educate our youth. What is the next phase of generative AI? Where are we going with GANs? What are emerging threats. Join discussions. Take mini classes. Contribute to articles and podcasts. Learn. Then share your experiences and knowledge.

The field of AI is advancing at an astonishing pace, with new breakthroughs, innovations, and discoveries emerging every day. It's crucial to keep up with the latest developments, trends, and research to remain competitive and stay ahead of the curve. By engaging in continuous learning and staying up-to-date, you can ensure that you are equipped with the knowledge and skills needed to succeed and thrive in this dynamic field.

Anyone can start with Online Courses and Self-Study ==> Enroll in online courses on platforms like Coursera, edX, or Udacity offering AI and machine learning fundamentals. ==> Utilize free resources such as YouTube tutorials, blogs, and AI community forums to supplement learning. ==> Complete projects and participate in Kaggle competitions to gain practical experience and showcase skills. ==> Pursue certifications like Google’s TensorFlow Developer Certificate to validate expertise.

Continual learning is the cornerstone of a successful career in AI, especially when starting from scratch. Throughout my journey, I've found that immersing myself in online courses and tutorials tailored to AI fundamentals was invaluable. Additionally, actively seeking out opportunities to apply newfound knowledge through projects or internships solidified my understanding and skill set. Embracing a mindset of perpetual growth and adaptation in the dynamic AI landscape ensures relevance and competence in the field.

"Deep Learning is a fascinating field that requires constant learning and adaptation. Staying updated on the latest research and technologies is crucial for success in AI. Engaging with the AI community through research papers, news outlets, and online forums is a great way to stay informed. Remember, continuous learning is key to staying competitive in this rapidly evolving industry." Let's keep pushing the boundaries of AI knowledge together!

Internships are excellent opportunities to learn AI on the job. Particularly if you are teaming with more experienced members to establish policies in support of AI governance and ethics programs. In parallel, take courses to learn the software basics and if you find it of interest, pursue.

??With experience you can showcase your learning being applied to real work. ??As a beginner look for internships to start with. ??Move on to showcase such body of work to and take up paid assignments or jobs. The sinner you have industry experience the faster you build your profile in AI.

To gain experience in AI, if you already have your fundamentals, participating in Kaggle competitions and join online forums, such as GitHub, can help you learn from others and share your own projects. This hands-on approach will help you build a portfolio and stay up-to-date with the latest developments.

??? Foundation First, Then Experience Start with mastering AI basics before pursuing internships or projects. This approach ensures a solid entry into the AI field, building your expertise step by step.

Internships and Entry-Level Positions are a great place to start from ==> Apply for AI internships or entry-level positions that require minimal prior experience.

Starting a career in AI without a background is feasible with dedication and strategic steps. Begin by acquiring foundational knowledge through online resources and courses, focusing on programming languages like Python. Dive into machine learning basics, engage in hands-on projects, and network with professionals in the field. Embrace challenges, learn continuously, and consider pursuing certifications or formal education to enhance your skills. With perseverance and a proactive approach, you can forge a rewarding career path in AI, regardless of your starting point.

The word 'background' itself is confusing. If background equals educational qualification, then I must state that AI is an evolving domain and the qualification earned a few years before might not even be sufficient today! If background equals to knowledge, then the knowledge (and skill sets) can be gained anytime! Hence, my suggestion will be to keep learning, keep practicing and keep exploring.

How you can start a career in the field of AI without any background? It’s the same advice for starting a career in any field without any experience or background - Don’t do it!! Seriously - why would I pay a photographer who couldn’t demonstrate capability using a camera. Or a web designer who wasn’t able to show me a portfolio of work. If you’re thinking of getting into AI as a career, don’t do it until you’ve built up some level of background, experience, and capability. Use the various tools yourself, experiment, write blogs, help small business owners integrate AI into their work, learn from it all, and slowly (but surely) you’ll be gaining experience and expertise which (when gained) you can use to help launch your new career.

Starting a career in AI without prior experience involves to Enroll in online courses & attend workshops to learn basics. Join AI communities & seek mentorship to network widely. Gain experience by participating in hackathons & internships while continuously learning. Build AI projects to showcase skills & contribute to open-source projects for hands-on experience. Pursue certifications & attend conferences to stay updated. Follow thought leaders & read publications for insights. Join study groups & seek feedback to improve. Collaborate with experts & explore job shadowing opportunities for practical exposure. Engage in networking events & join clubs to expand connections. Explore apprenticeship programs & continuous learning is essential.