How can you display your Machine Learning course accomplishments?

Creating a portfolio project based on your learnings is the best way to not only solidify your knowledge but also to your showcase your skills. Starting simple is very nice, however you should try to challenge yourself on those projects to do things you never did before. Going with a new tool, doing a different architecture etc. Don’t worry if the project will take more time, because the amount of things you’ll learn will even surpass the course you just did.

These points are quite noteworthy. I'd like to emphasize that Kaggle serves as the ultimate showcase arena for data scientists. The platform is meticulously designed to empower data scientists, offering top-notch tools and services. Another valuable aspect is the ability to deploy models and make them accessible to a wider audience online. Modern technologies like Streamlit, along with other options, have made model deployment seamless. Lastly, it's crucial to distill the final outcomes and presentations into easily understandable, concise, and accessible terms for non-experts.

Showcase through a website, blog, medium or LinkedIn newsletter’s the abilities acquired in your ML course. Have an Open-Source Code GitHub repository where other people can see what have you accomplished and how. Be open to comments and suggestions from other professionals, but always leave your blueprint in your portfolio.

Engaging with peers in the field can be a valuable extension to your portfolio. By actively participating in peer reviews and collaborations, you not only gain insights from different perspectives but also showcase your analytical and problem-solving skills. Partner with fellow data enthusiasts on joint projects. These collaborations can lead to innovative solutions and demonstrate your teamwork abilities. Showcase these projects in your portfolio, highlighting your specific contributions. Offer to review code or ML models of peers and seek the same in return. This iterative feedback process can help refine your projects and provide a holistic view of your work, showcasing your commitment to continuous learning and improvement.

Showcasing your machine learning skills involves building a diverse portfolio of projects, contributing to open source, participating in Kaggle competitions, maintaining a personal blog, and creating a GitHub showcase. Emphasize real-world impact, collaborative projects, relevant certificates, and update your LinkedIn profile to reflect your expertise. Highlight both technical and soft skills to make a lasting impression…

While technical proficiency is crucial in the realm of Machine Learning, don't forget to emphasize your soft skills and experiences that illustrate your ability to work collaboratively and communicate effectively. Highlight instances where you've tackled complex challenges, perhaps where you had to think outside the box or come up with innovative solutions using Machine Learning. Machine Learning is a rapidly evolving field. Highlight any additional courses, webinars, or workshops you've attended since completing the course, showing your commitment to staying updated. Depending on the position you're applying for, customize your resume to emphasize the most relevant skills and experiences.

It is not really advisable to create additional blog posts or personal websites on ML if you do not have anything original to contribute to the field. There is so much content on AI and ML already in the public domain and an additional blog post would be meaningless if you don't have a seminal concept that you have developed on your own. I would advice against starting a new blog page and rather continue sharing useful resources which are already present in the public domain through LinkedIn or Twitter.

Make your portfolio uniquely yours as the public facing component of your daily journaling. Future you, your collaborators, and potential employers will see samples of your work firsthand, greatly increasing your prospects. Publicly solicit feedback from mentors. Constructive criticism will showcase how you refine your skills and find ways to raise the bar on quality. Document your learning journey. A portfolio also provides an indication of your capacity to collaborate, and that you give due credit to your sources. Demonstrate your ability to deliver value. Ask the right questions. Innovate. Document your workflow, including problem statement, sources, code, and conclusions. Engage on several forums like Kaggle or LinkedIn.

Certifications are a game-changer, speaking from personal experience. They open doors to projects and jobs with ease. In the dynamic world of ML, staying current is essential. Certificates not only validate your skills but also showcase your commitment to the latest trends. I would recommend starting free-of-cost (Audit) specializations on Coursera from organizations like Stanford University and DeepLearning.ai

I found the Coursera program on 'Deep Learning Fundamentals' by Andrew NG (Stanford University) extremely useful to get a good grasp of ML concepts. Once you have completed the course, you can obtain a certificate which you can then plug into your LinkedIn profile. Most ML companies recognise the course and adds to your overall credibility. Another good Coursera course is the ML Ops course taught by the Professors at Duke University which mainly focuses on creating clean datastreams and data pipelines for further processing and operationalisation. If one completes both courses and get the valid certifications embedded onto one's LinkedIn profile, it would greatly increase the chances of career improvement.

I was able to share my Machine Learning projects learned during the time working through my AI/ML Python certification from the University of Texas - Austin. Moreover, what I realize that to stand out even more is to create your own personal project of passion that will showcase your unique talent for finding unique insights and tool application towards relevant data domains. Personalization is key in presenting to the world your ability in finding the holy grail "signal in the noise," which is golden to hiring managers. Akin to the statement above, Kaggle is a great place to start cutting your teeth with your newly minted set of Data Science skills.