You're a Machine Learning professional. How can you ensure your skills stay up-to-date?

I believe that Machine Learning always requires staying up-to-date and I would use these strategies: - Continuous learning - Innovative projects - Attend workshops and conferences, online communities - Collaborate on research - Experiment with new tools - Seek mentorship

Machine Learning is evolving at a rapid pace, most visibly through the advent of LLMs. There are new Python/R packages being released, some of which are more efficient than their predecessors (like Nixtla's autoARIMA implementation that's 20x faster than pmdarima!). Exciting applications, that earlier used either purely statistical or adopted a non-analytical approach, are now being streamlined into machine learning workloads. A good way to stay on top of it all is to read widely and implement these hands-on on your favourite problems or data sets. In terms of outlook, be flexible and open to learning new technologies, and adopt a continuous learning approach. Essentially, learn "how to learn" itself which will be a journey unique to you.

Online courses offer accessible and flexible learning opportunities in machine learning. Platforms like Coursera, Udemy, and edX provide diverse courses on data science, deep learning, NLP, and computer vision. Tailor courses to your skill level, duration, and format preferences. Gain theoretical knowledge, practical skills, and certificates to enhance your resume and portfolio.

Think of online courses as friendly guides to machine learning. They're like interactive storybooks that teach cool skills. Pick the ones you like, and soon you'll have a skillful adventure!

Transitioning into the AI space was a dream of mine for a long time. First step for me was to learn, I took professional courses to do this. Second and more challenging task was to stay up to date. If your day time job is doing something else, keeping up to date with a different domain is harder. Other than obviously having side projects to practice, I started following top voices and started signing up to newsletters to get the best information. If you go through courses first and then do this, things will make a lot more sense and it will be relatively easier to understand the background when you read latest articles etc.

Firstly stay updated to latest tech news and inventions Follow research scholars scientists in this domain . Make sure to practice projects on your own . Try to take challenge and work on it . Follow famous persons in this domain in LinkedIn platforms. Ask questions and participate in webinars workshops etc

Machine Learning is a rapidly growing field and it is important to keep yourself updated with latest advancements in this field. Joining online communities like LinkedIn, Kaggle, Medium etc. can help to learn about the emerging ML topics. Experts from different fields share their opinions through blogs, newsletters, video tutorials and live events on these platforms. Listening to others and reading about their opinions help to have multidimensional perspective on problem solving. You can share your own experiences and challenges also. It provides an opportunity to network with others and seek their mentorship and feedback on your ML journey.

The most exciting work for me happens outside of official jobs or assignments. That's where those personal projects come in. I get to pick a problem that genuinely excites me, maybe something outside my usual domain, and dive headfirst. It's the perfect sandbox to experiment! Want to try out that hot new library I've been reading about? Or tackle a dataset that looks messy just for the challenge? Personal projects mean I set the pace. The freedom to make mistakes without high stakes lets me take risks and push my own boundaries. Plus, let's be honest, it's these kinds of projects that make for the most interesting portfolio pieces.

Portfolio, it's not just a collection of projects, it's our story as a Machine Learning professional. I'm making a point to keep it up-to-date and polished. New projects, completed courses, even insightful blog posts I've written – anything showcasing my growth goes in there. It's tempting to focus solely on the technical stuff, but I've learned a clear storyline is important. How have I progressed? What skills am I actively developing? A well-organized, visually clean layout is key too – it shows attention to detail.

Online courses provide an overview of current trends in machine learning (ML) but often miss out on in-depth knowledge and the newest studies. To remain informed: Engage with ML communities on LinkedIn and sign up for pertinent newsletters. Go further by subscribing to newsletters offering comprehensive summaries of the latest research in your field on a monthly and annual basis. Concentrate on reading summaries and delve into papers that resonate with your areas of interest. Explore new technologies and discuss your insights with peers. This strategy will keep you abreast of the continuous advancements in the ML landscape.

No mundo dinamico que vivemos é um desafio para todo profissional se manter atualizado com as principais inova??es e tecnologias disponíveis no mercado. A revolu??o promovida pela aplica??o de Inteligência Artificial e Machine Learning nas mais diversas áreas do conhecimento torna ainda maior o desafio para os profissionais atuam ou desejam atuar neste segmento. Aproveite as dicas apresentadas no artigo e se mantenha conectado com as principais empresas provedoras de solu??es em dados e Inteligência Artificial.

The most reliable method to stay up-to-date with Machine Learning news is to read recently published papers in the field. Given the rapid progress in ML, courses and textbooks have limited capacity to keep you fully updated.