What are the best ways to gain hands-on experience with AI as an entry-level professional?

Para adquirir experiencia práctica en Inteligencia Artificial (IA) como profesional de nivel básico, los cursos en línea son una excelente opción. Plataformas como edX y Udemy ofrecen una variedad de cursos que cubren temas desde los fundamentos de la IA hasta el aprendizaje automático y la ciencia de datos. Un curso destacado es "Elementos de la IA", creado por MinnaLearn y la Universidad de Helsinki, que combina teoría con ejercicios prácticos y puede completarse a tu propio ritmo, otro es el de "Fundamentals of Deep Learning for Computer Vision" de NVIDIA, que se ha posicionado como una referencia en la industria de la IA. El aprendizaje continuo y la adaptabilidad son esenciales en el campo de la IA, dado su rápido avance y evolución.

Below are some good online courses(free), which can help you to get started: 1. Introduction to Generative AI by Google Cloud 2. Introduction to Artificial Intelligence by Stanford University 3. Career Essentials In Generative AI by Linkedin 4. AI Foundations for Everyone by IBM

Dive, Discover, Develop! 🤖🌐 I'd recommend starting with online courses to build foundational knowledge, as described in Andrew Ng's AI for Everyone. Engaging in hackathons and competitions like those on Kaggle provides real-world problem-solving experience. Undertaking personal projects allows for creative exploration and practical application. Seeking internships and mentorships offers guided, hands-on experience and invaluable networking. Joining online communities, like GitHub or Reddit's AI forums, provides insights, support, and collaboration opportunities. Each step is a building block in your AI journey.

Self-guided online courses are a great tool if you want to be able to tackle content at your own pace - but make sure to expand on the provided assignments/projects and build something that is in a domain you are passionate about! Reworking existing solutions to fit another domain is something hiring managers will be looking for!

Platforms like Coursera, Udacity, edX, and Kaggle provide a plethora of courses covering the fundamentals of AI, including essential algorithms, frameworks, and tools. These courses are designed not only to impart theoretical knowledge but also to offer practical exercises, projects, and assignments, facilitating real-world application of AI concepts. This approach is particularly beneficial for grasping best practices and ethical considerations in AI. Furthermore, these courses cater to a wide range of topics, skill levels, and durations, allowing learners to tailor their educational journey to their specific interests and career aspirations. I also state what I've said before: I believe in the AI programs at some of the elite universities

Engaging in hackathons and competitions is an effective way to acquire practical experience. These events provide a platform to tackle specific challenges within a limited timeframe, collaborating with fellow enthusiasts and experts. They help sharpen problem-solving, ingenuity, and collaborative skills while exposing participants to novel ideas and technologies. These events offer valuable networking opportunities, connecting you with mentors, sponsors, and peers. Platforms like HackerEarth, Devpost, and DrivenData host many such opportunities, enabling entry-level professionals to dive into AI and emerge with tangible skills and knowledge, setting a solid foundation for a successful career in this dynamic field.

In the rapidly changing AI landscape, practical experience is key. Beginners can dive in with these strategies: 1. Personal Projects: Tackle AI problems you find intriguing using relevant tools and datasets. 2. Online Courses: Platforms like Coursera or Udemy offer hands-on AI courses. 3. Data Competitions: Engage in real-world challenges on sites like Kaggle. 4. Open Source Collaboration: Contribute to AI projects on GitHub for real-world experience. 5. Internships: Seek roles in companies or labs for professional guidance in AI. 6. Community Involvement: Join AI groups for networking and mentorship. These approaches help entry-level professionals gain essential AI experience.

Here are some ways to gain hands-on experience: 1. Take online courses on platforms like Coursera or edX. 2. Participate in Kaggle competitions for practical challenges. 3. Build personal AI projects to showcase your skills. 4. Contribute to open-source AI projects on GitHub. 5. Attend AI meetups, conferences, and network with professionals.

For entry-level professionals, hackathons and competitions are like immersive AI bootcamps. Here's how they pack a punch: Hands-on learning: Apply your knowledge, experiment with tools, and solve real-world challenges – all under pressure! Teamwork and mentorship: Collaborate with diverse talents, learn from experts, and build your network. Testing and validation: Get instant feedback, benchmark your skills, and showcase your work to potential employers. Rewards and recognition: Win prizes, gain media attention, and boost your career prospects. Remember: Choose wisely, step outside your comfort zone, and learn by doing. So, dive in, collaborate, and have fun as you unlock your AI potential!

One of my engineers believes in this, the other is on the fence. I lean more to the skeptical side. So long as you do your due diligence on the competitions, they can be of great value. Others I know have done these and its work companies would pay significantly for, that they are getting for free. So while there is value here when you're starting out, be sure to not give away too much. Your talent has value in AI, even if just starting out.

If you were to select one option from this list, I would strongly urge you to select this. Personal projects are not only a great way to showcase your existing skillset, but also allow you to express your personality and passion! Having a problem in your day to day and then using AI to solve it, or at least help solve it, is an amazing way to communicate both your proficiency and your interest in AI! You can focus projects on specific skills, or even showcase the breadth of your talent by showcasing multiple skills at once! If you wrap the project in VCS, or use experiment tracking, or deploy it, you can also show your ancillary skills!

Como profesional novel en IA, obten experiencia práctica con proyectos personales. Empieza con algo simple como un chatbot o sistema de recomendación básico. Luego avanza a proyectos más complejos de PNL, visión artificial o aprendizaje profundo. Usa herramientas como Python, TensorFlow y Keras. Documenta bien tu código y proceso para crear un portafolio. Comparte proyectos en GitHub y obtén feedback. Participa en competencias de Kaggle. Colabora en proyectos open source. Sigue incrementando complejidad. La práctica repetida con proyectos reales es la mejor forma de obtener experiencia en IA.

Go AI hands-on: Project playground: Build your own AI app, analyze your cat pics, or predict the next viral trend. Show off your skills and passion! Hackathon blitz: Team up, solve real problems, and win prizes. Learn fast, network, and unleash your creativity! Course climb: Level up your AI knowledge with online courses. Master new tools, explore applications, and get certified. Open-source boost: Join the AI community, fix bugs, and learn from pros. Contribute and grow together! Meetup magic: Connect with local AI enthusiasts, learn from speakers, and find mentors. Network your way to success! Start small, learn by doing, and have fun! AI awaits your exploration.

Personal projects and discovery are the best way to learn in a frontier innovation cycle. Classes tend to be better when an innovation cycle is more mature. AI, particularly generative AI, is at a stage where you are best served with learning by doing. Use all of the relevant products to understand how they perform. Dip into developing a prototype solution using a code repository or no-code solution. This will give you a framework for understanding how the technology works, what it can do, where there are limitations, and what opportunities are available. With that context, you can then go deeper into the domain you have started exploring or use that experience to identify your next area of discovery.

Working on personal projects can be one of the most effective ways to gain hands-on skills. When just starting, real-world experience matters. Personal projects allow you to experiment, apply your learning, and create pieces for your portfolio—all on your terms. Start by thinking about AI application areas that interest you. Building projects related to your domain passions will stoke engagement. Look for ideas and datasets on platforms like Kaggle or browse through open-source code on GitHub. Recreating existing projects is a great way to get started. Set clear goals and milestones to maintain focus and measure progress. Document your work thoroughly. This creates material to showcase what you’ve built.

Pursuing internships and mentorships is key for entry-level professionals to gain tangible AI experience. These opportunities offer direct access to industry insights and real-world challenges, fostering a deeper understanding of AI applications. They provide a platform for practical learning and growth under expert guidance, essential for skill development and professional networking. Utilizing resources like Internshala, Indeed, or LinkedIn for such opportunities is a strategic move for those starting their AI journey, demonstrating a proactive approach in a PMO-driven AI landscape.

internships and mentorships offer invaluable hands-on experience in AI by connecting you with seasoned professionals. Working alongside or learning from experienced practitioners provides practical knowledge and insights beyond online courses or personal projects. The guidance, feedback, and support from mentors or supervisors enhance your growth. Platforms like Internshala, Indeed, and LinkedIn, as well as networking with professors, alumni, or friends, are effective ways to discover these enriching experiences, fostering a bridge between academic learning and real-world application in the dynamic field of AI.

💡 Engaging in internships and mentorships is a key step in gaining practical AI experience, as I've learned from friends and experts. These opportunities offer a chance to work with seasoned professionals, blending practical knowledge with real-world insights. Beyond just theory, they provide exposure to actual scenarios and challenges in AI. I've been told that LinkedIn is an excellent platform for finding such opportunities, along with sites like Internshala and Indeed. Leveraging your network, including professors and alumni, can also be highly beneficial. Remember, the mentorship or internship you choose can be a pivotal moment in your AI journey, accelerating both learning and career prospects.

Nothing great is built alone. And it's easier to learn from other people's mistakes than your own. A mentor who went to all of that and wants to share what they would have loved to know in your position. I have not met so far somebody great in their field who doesn't want to help others grow. So reach out to the people who wrote the papers you admire, ask people for advice and just apply for internships that you feel will make you grow.

Tanto las pasantías como las mentorías son herramientas extraordinariamente poderosas para el desarrollo profesional. Brindan oportunidades únicas para el aprendizaje, el crecimiento personal y el establecimiento de una sólida red profesional. Para maximizar su valor, es importante elegir la oportunidad adecuada, establecer objetivos claros y adoptar una actitud proactiva y comprometida. Beneficios de las pasantías: Experiencia práctica, Aprendizaje de habilidades relevantes, red de contactos profesionales. Beneficios de las mentorías: Orientación personalizada, aceleración del aprendizaje, desarrollo de carrera, apoyo y motivación. Consideraciones clave: la elección del mentor, comunicación abierta, compromiso y respeto.

1. Reddit: - Subreddits like r/MachineLearning or r/deeplearning offer community discussions, project showcases, and advice from experienced practitioners. - Ideal for real-world insights and community support. 2. Stack Overflow: - Get answers to specific coding problems in AI. - Helpful for troubleshooting and learning from others' coding challenges. 3. Medium: - Articles by AI professionals and enthusiasts. - Offers deep dives into AI topics, case studies, and industry trends. 4. YouTube: - Channels dedicated to AI tutorials, news, and discussions. - Visual and practical learning for concepts ranging from beginner to advanced levels.

As an entry-level professional aiming for hands-on AI experience, one can look towards online communities such as Kaggle, GitHub, and Stack Overflow. Participating in collaborative projects, competitions, and discussions to build practical skills. Leveraging LinkedIn by following AI influencers, and joining relevant group can help in staying updated with evolving trends. Engage with professionals in the field, seek advice, and potentially find mentorship opportunities. LinkedIn's networking capabilities can catalyze learning, connecting you with the AI community, and paving the way for valuable insights and career opportunities in the growing landscape of artificial intelligence.

💡 Online communities and resources are great for hands-on AI experience. Platforms like Reddit, Stack Overflow, Medium, and YouTube allow you to engage with AI experts, share ideas, and stay updated on trends. These spaces are invaluable for expanding your knowledge and network in the AI field. I also strongly encourage joining & participating in discussions in LinkedIn Groups.

Reddit, LinkedIn, Medium are all places I used in the beginning. Not so much YouTube but it has value and even more so now. GitHub is a personal favorite of mine and my team as it has so much info and projects there. Look for some of the articles on Medium that AI professionals are writing. There was a recent one from one of the heads of AI at Microsoft that was very informative without being a marketing piece for them. Another of my favorites is here on LI: a woman whose career I have followed since her beginning and always has great info and data in her posts.

The OpenAI Cookbook is ideal for entry-level AI professionals, offering comprehensive, practical learning with up-to-date content. It covers a broad range of topics, from basic concepts like token counting with TikToken to advanced applications such as processing videos with GPT's visual capabilities and building agents with the Node.js SDK. Its clear explanations and real-world focus make complex AI topics accessible and applicable for beginners.

The main point is this: Beware of FOMO (Fear of Missing Out). The widespread use and sharing of AIs can have side effects on beginner-level users. As a whole, the scene melts into their thoughts the idea that you need to test all tools, be on top of all channels of information, and not miss a bit of what's being said. But the truth is that most tools have the same foundation, just with different instructions. So, the best approach is to understand how this foundation works so that you can give those instructions yourself and adapt them to your needs. With focus. By being selective. Moreover, following experts who publish quality, not quantity can be an excellent path.

I would recommend to think of some use cases in daily work and implement it. In current scenario, AI can be applied in any domain. This would also help one to analyze the strengths and capabilities of AI. Moreover, I would recommend to practice and hands on with the AI algorithms, used in your project, from scratch. This will help in understanding the basic concepts and also help in identifying future AI projects based on the problems.

In my view, to gain hands-on experience with AI as an entry-level professional, take online courses and tutorials, build personal projects, engage in AI communities and forums, attend AI workshops and conferences, consider internships and mentorship programs, utilize AI tools and libraries, and showcase your work on portfolio websites. This will demonstrate your skills and experience to potential employers and help you stand out from the crowd. All the best !

En mi opinión involucrarte en proyectos de IA en tu trabajo actual puede ayudar mucho. Muchas empresas están adoptando IA, así que habla con tu equipo sobre cómo podrías contribuir. Identifica procesos que podrían optimizarse con IA y propone soluciones. Aunque tu rol actual no esté directamente relacionado, esta es una gran oportunidad para obtener experiencia práctica. Construye relaciones y aprende de compañeros con experiencia en IA. Enfócate en aplicar IA para resolver problemas reales de negocio. El trabajo en proyectos reales con stakeholders genuinos es invaluable, aun si no usas las tecnologías más avanzadas. ¡A por ello!

I would suggest to first take a few courses that also include labs and then try to find an internship. Don't expect to be paid during the first internship. Your focus should be on gaining hands on experience!