Como você pode demonstrar sua experiência de domínio por meio de um portfólio de aprendizado de máquina?

The best way to showcase a skill set in ML is creating visibility of a result or value-added to any domain. It is imperative a combination of several different of so-called hard and soft skills. The first approach is to identify a gap existing in any domain that ML would fit very well. Since developing ML is not an isolated task, the soft skill in creating networking and communication with other people are key to success. For example, it is important to establish good relationship with business/domain expert, data engineer, software engineer, UX/UI designer, compliance (privacy and regulation), security, to name a few. In my view, ML engineer is highly valuable for both hard and soft skills together thus technical expertise is not enough.

Building a machine learning portfolio is a great way to showcase your expertise in a specific domain like healthcare or finance. Start by selecting a focus area and developing relevant projects. Make sure your work is presented in a visually engaging way, explaining your machine-learning techniques and why you chose them. You can use tools like Jupyter Notebooks for this. To further establish your authority, you could include domain-specific blog posts that discuss machine learning topics. Collaborating with experts in your chosen field can enhance your portfolio and show your teamwork skills.

I feel the best way to demonstrate your expertise not only in machine learning but any tech domain is through open source. As most of the applications in any enterprise is majorly built on open source distributions/softwares, contribution to it will put you in limelight. Some of the major players in open source ML domain are: HuggingFace, Spacy, Langchain, OpenCV, Paddle, Ultralytics etc. However we can also boost our portfolio by doing the below. - Blogs: Write blogs and tutorials on ML/AI components - Kaggle Contributions: Participate in as many as contributions as possible - Low code/No code: If you are good in programming, use it to create simple libraries low code that others can use - Hackathon - Gain knowledge from Research papers

PASSION: Choose what excites you. Genuine interest drives commitment and innovation. BACKGROUND: Utilize past experiences or studies. This aids in applying ML solutions to real-world scenarios. DATA ACCESS: Secure reliable datasets. Authentic data boosts project relevance and insights. CAREER GOALS: Align domain with where you aim to be. It amplifies your value proposition in specific roles. RESOURCES: Master domain-specific tools to elevate project depth. Your chosen domain should not only reflect technical prowess but also show an understanding of sector nuances. It distinguishes you from the crowd.

RELEVANCE: Prioritize datasets and projects aligning with current industry trends and demands. This highlights your capability to solve contemporary challenges. SOURCES: Explore platforms like Kaggle, UCI ML Repository, or DrivenData for domain-specific datasets. DIVERSITY: Showcase varied complexities - from foundational to advanced. This paints a comprehensive skillset spectrum. PERSONAL PROJECTS: Don't shy from incorporating personal hobby-related data analyses. It reveals creativity and passion. DOCUMENTATION: Pair projects with clear documentation, code comments, and intuitive visualizations. It ensures clarity of approach and thought process. FEEDBACK: Engage peers for reviews.

CLEAN CODE: Employ PEP8 standards for Python or respective guidelines for other languages. It underscores professionalism and ease of collaboration. VERSION CONTROL: Utilize Git for iterative development, showing a clear history of your model's evolution and problem-solving aptitude. RESULTS VISUALIZATION: Incorporate charts, confusion matrices, or ROC curves to visualize model performance, making it digestible for diverse audiences. DOCUMENTATION: Include Jupyter notebooks or Markdown files. They serve dual roles - code execution and comprehensive explanations, enhancing clarity. CONTINUOUS IMPROVEMENT: Document model refinements, underscoring a commitment to achieving optimal outcomes and dedication to the craft.

TAILOR YOUR DELIVERY: Understand your audience. Professionals appreciate metrics; laypeople need visuals. STORYTELLING: Frame results as stories. Begin with the problem, dive into the approach, and unveil the solution. MULTIMEDIA: Create engaging video summaries or infographics for a broader reach. BLOG REGULARLY: Break down complex topics; platforms like Medium or Substack are ideal. FEEDBACK IS GOLD: Engage your audience. Comments can provide insights and foster collaboration. BE CONSISTENT: Regularly update projects. Reflecting on growth and current trends makes you more relevant.

EMBRACE FEEDBACK: Regularly solicit input. Constructive criticism from mentors, peers, or hiring managers can elevate your work. CONTINUOUS LEARNING: Incorporate new techniques and methodologies. Stay updated with ML literature, online courses, and workshops. SET CLEAR GOALS: Define short and long-term aspirations. Whether mastering a new algorithm or targeting a specific role, let it guide updates. FLEXIBILITY IS KEY: As tech evolves, re-evaluate models and techniques. It's not about quantity, but the relevance and depth of each project. SHOWCASE GROWTH: Highlight your journey. Displaying evolution in skills and methodologies can impress potential collaborators or employers.