Here's how you can boost your career by contributing to the Machine Learning community.

Find one or two close-knit forums, such as Cohere for AI Research or the TWIML Community. Additionally, join a book reading group and gradually take on a leadership role by sharing your insights. This approach will not only help you share your knowledge with others but also allow you to identify and address potential gaps in your understanding.

My suggestion to boost one's career in ML is that one should engage in forums and also publish blogs on Medium or LinkedIn, share projects on GitHub, and speak at conferences like NeurIPS or ICML. Collaborate on open-source projects, and build a strong professional network.

Boost your career in plastic welding machine learning by engaging in open-source projects, attending conferences, and writing blogs or tutorials. These activities enhance your skills, expand your network, and increase your visibility in the ML community, opening up more professional opportunities.

Forums are generally good sources where quality information is shared. If you find a good article, figure out communities which the author follows and join them. I applied a similar procedure once and stumbled upon a forum where motivated folks of all ages share articles everyday which include research papers, news, ideas, experiments, valuations and what not. It is important to keep updated about all the happenings because Analytics/Machine Learning is an extremely fast growing space which is evolving on multiple fronts. Some forums also conduct meetups; and if these are attended your network grows well and you end up meeting persons with diverse mentalities and this helps you further your understanding.

Contributing to the machine learning community can significantly boost your career by providing networking opportunities, enhancing your skills, and building your reputation. Engaging in open-source projects, writing blogs, and sharing knowledge through tutorials or webinars helps you stay updated with the latest advancements and learn from others’ experiences. Active participation can establish you as an expert, leading to speaking engagements, consulting roles, and higher-level positions. Additionally, contributions to notable projects enrich your professional portfolio, while giving back to the community brings personal satisfaction and fosters a collaborative and innovative environment.

Contributing code is possibly one of the best ways to boost your ML career. With more and more models and frameworks being released as open source projects on GitHub, there is a growing demand for contributors as well. Going through these codebases can help you understand the what, how and why behind some of the most popular machine learning projects today. Once you have understood the codebase thoroughly, you can contribute by fixing bugs, adding new features and so on. This helps solidify your profile and proves to any employer that you have what it takes to undertake complicated ML-related tasks.

One easy way to get started is by contributing to the GitHub repository for the source code of the book you are reading. This will complement your learning and boost your confidence. Gradually, you may find yourself interested in contributing to other open-source projects, further enhancing your skills and visibility in the community.

Contributing with code is a great way of not only connecting with ML folks around the world but also a way to showcase your knowledge to recruiters. Many datasets are publicly accessible these days on Kaggle or UCI machine learning repository. One can work around these dates and make it available to others either through Kaggle or on their GitHub accounts or both. Coding also helps the ML engineers and data scientists to practice the concepts they have learnt.

Contributing to open source projects is without a doubt one of the best ways to both grow and give back to the community. If you're someone like me who's taking their baby steps into machine learning, don't stand back because you're too nervous to contribute. Instead, take the plunge and start small. Start Small with minor contributions like fixing typos in the documentation, adding comments to the code, or writing simple test cases. This helps you get familiar with the project and build confidence. Then once you are ready, create pull requests for your contributions. Be open to feedback and be willing to make improvements based on the maintainers' suggestions and remember to stay persistent.

Publishing my research was a pivotal moment in advancing my machine learning skills. The first time my paper was accepted, I felt a profound sense of achievement. Sharing my findings with the community not only added to the collective knowledge base but also sparked new ideas and discussions among peers. The process of writing and refining my research honed my ability to communicate complex ideas clearly and effectively, a skill that proved invaluable. Each published work bolstered my professional standing, attracting attention from potential collaborators and opening doors to exciting new opportunities. This journey of publishing research has been instrumental in my growth and recognition in the machine learning field.

I will use myself as an example, I'm a bit new to Machine Learning and Artificial Intelligence as a whole, but I do a lot of research and the research makes me a good contributor to the Machine Learning community and also makes me better in Machine and Deep Learning.

Publishing original research is an invaluable endeavor for anyone in the machine learning community. By disseminating your findings, you not only contribute to the collective knowledge base but also inspire further innovation and exploration. Writing papers or articles refines your ability to convey complex ideas in a clear and compelling manner, a skill that is highly prized in any professional field. Additionally, having published work significantly enhances your professional reputation and can lead to new and exciting opportunities. Through publication, you not only solidify your expertise but also play a crucial role in advancing the field of machine learning.

Publishing is always important to portray your skillset to colleagues, future employers, and aiding in collaboration. This also helps in climbing the academic/ corporate ladder.

Publishing research can significantly boost your ML career. Share your findings in reputable journals, present at conferences, and write blog posts. This establishes your expertise, keeps you at the cutting edge of the field, and connects you with other professionals and opportunities.

Mentoring is a way of engraving long-term knowledge. By contributing to the machine learning community, the mentor can also gain valuable insights from the community's perspective, as well as valuable information from their questions and feedback. This also creates a vast professional network that helps boost their careers.

The ability to mentor others on a given matter requires teachers to understand topics a lot better. Some people grasp topics easily but some people need simpler way of explanation. And mentoring these people pushes your limits to and even beyond their limits.

Tell me and I forget. Teach me and I remember. Involve me and I learn. -Benjamin Franklin Top voices and the smartest professionals in the field (including the godfathers of AI) are teachers at their very heart. Apart from the yearning to want the field to grow, teachers/mentors also want everyone around them to walk with them and contribute to the field along with them. Teaching not only solidifies concepts for us but also talking to different minds helps us look at basic concepts from newer perspectives which gives mentors better ideas to approach and research simple ideas. No better example than looking at top scientists who are also the best teachers.

I got the chance to mentor a person who was new to AI and ML a few years back. Collaborating with them, I witnessed the growth of my own ML comprehension as I clarified ideas and approached issues from their point of view. Together, we achieved remarkable progress and enhanced my leadership abilities. Being a mentor was really fulfilling and helped me build important new connections. I wholeheartedly endorse it if you have the opportunity!

From my experience, mentorship greatly benefits both career development and networking, as does any other free-of-charge work. Objectively, mentoring someone builds a bond with a person who might outgrow you or land a job at a company you’re interested in, giving you a potential foot in the door. Additionally, teaching helps you organize and reinforce your own knowledge. By walking someone through your thought process, you clarify your own understanding and may arrive at solutions more quickly.

Public speaking can greatly boost your career in machine learning. While technical skills are important, being able to clearly explain complex ideas to others is what sets you apart. Speaking to people and finding a common way to explain things is difficult, and only true experts can do it well. This skill shows you not only understand the subject deeply but can also lead, teach, and inspire others. By improving your public speaking, you can advance your career and help more people appreciate and understand machine learning.

Public speaking at conferences, meetups, or webinars is an excellent avenue for contributing to the machine learning community. Presenting your work or insights not only helps you gain confidence and enhances your communication skills but also provides a platform to share your passion and expertise with a broader audience. This increased visibility can significantly elevate your presence in the field. Additionally, engaging with the audience after your talks can further expand your network and influence.

Speaking at conferences is a fantastic opportunity to share your exciting work with the community. You don't need to spend much time preparing since you've already put in a lot of effort into your research. Plus, sharing your knowledge allows you to receive valuable feedback and new perspectives, helping you learn as much as your audience. In exchange for your time put into presentation you become more recognized and get great chances to expand your network.

I have spoken at multiple conferences as invited speaker and also as presenting my own work. In both cases I could connect to more number of people with interests similar to me which later became connections. A lot of networking happens over the food. I have hired people this way.

Yes, presenting at conferences or meet up’s helps one to learn more about the subjects and allows an opportunity to share your perceptions.

Writing blogs can significantly boost your Machine Learning career. Share your insights, tutorials, and experiences to establish yourself as an expert. It helps you articulate your knowledge, reach a broader audience, and engage with the ML community, opening doors to new opportunities and collaborations.

Supervised Learning: Algorithms learn from labeled data and are tasked with making predictions or classifications based on that data. Unsupervised Learning: Algorithms learn from unlabeled data and are used to find patterns or groupings in data.

Writing blogs about machine learning topics is another effective way to contribute to the community. Through blogging, you can articulate your thoughts on recent developments, tutorialize complex concepts, or share experiences from your own projects. This not only benefits readers who are looking to learn but also reinforces your own understanding. Additionally, consistently producing quality content can establish you as a thought leader in the space. Having written blogs on Medium, I have experienced firsthand how this platform can amplify your reach and impact, allowing you to engage with a broad audience and contribute meaningfully to the ongoing discourse in machine learning.

Blogs are an effective way of sharing your thoughts about any topic. Moreover, it is a powerful way to contribute to the community and enhance your own understanding. This not only reinforces our knowledge but also helps to stay aligned with the current trends. Consistently producing quality content can broaden our reach and make an impact in the machine learning community.

Personally I have written architecture overviews that explains the concepts behind the architecture and it's working and I've learned and improved so many skills during the process like creating a compelling powerpoint presentation, reading a research paper (what to look for and avoid), and you also learn concepts from those papers. In conclusion, I would say this is really important and one should spend sufficient amount of time publishing some kind of content if not original research.

Joining ambassadorship programs can be great way! For women in ML, you can join Women in Data Science as an ambassador and start hosting events to network with more and more ML enthusiasts. There are multiple other ambassadorship programs offered by Microsoft Learn, Adobe, NVIDIA, Tableau, DeepLearning.AI. Look into these, it would be really helpful to become a part of such vibrant communities.

The pillars of growth in the Machine Learning community are collaboration, sharing and learning. The desire to share knowledge is a motivating factor for other people to do so as well. Collaboration ignites new concepts and speeds up innovations. A person learns from the experiences and expertise of others for collective development. Together we can accomplish more than any one of us alone could do.

Something not discussed here is the value of sharing educational materials directly on LinkedIn. This platform is excellent for learning, especially from real-life examples rather than theoretical materials. While respecting your company’s privacy rules, try to share your progress, the problems you were addressing, and the creative (or not so creative) solutions you came up with. It’s also important to share your failures. We often focus on our successes, but it’s equally important to show what didn’t work, why it didn’t work, and the lessons you learned from it.

I would strongly suggest pursuing a graduate course from a college / university with a strong AI/ML department. While there are a plethora of online courses available that can get you a job, it's my belief that when you pursue a graduate course from a university, you will automatically become part of a community/alumni network of students who go on to achieve some really cool things in the ML domain. Graduate alumni networks are very close-knit and there is a feeling of "My tribe" among the group.