What are the best ways to track your AI skill development?

As much as an advocate as I am for knowledge gained from courses taken at educational institutions, I believe wholeheartedly in self assessment and quizzes. Most of mine came from me taking notes from AI courses at DeepLearning AI, edX, Udemy, and yes even advanced versions of CGPT. But I did them frequently so I could memorize as much as possible without overload. It’s a daily routine for me and at first, no I hated it. However, perfect practice makes perfect or as close as I can get, and I learned to appreciate the process and thoroughly enjoy the challenge of this. It helps you immensely and if you’re in the business of AI and consulting clients in it, it will make you an even better source for them and they will appreciate also.

Self-assessment quizzes assist track AI skill improvement by reviewing essential concepts and methodologies, identifying knowledge gaps, and reinforcing memory.

I agree, self assessment quizzes can help test the understanding of new things learnt. But a better techniques is to try to teach the learnt concepts to some one else using analogies and a simplified language. As if you are trying to explain it to a 5 years old. This is called the Feynman Technique and its very powerfull method to learn new things not only AI or technical methods

Um recurso que eu utilizo é fazendo um inventário de conhecimento, avaliando o que eu já havia desenvolvi de soluções no passado. Geralmente eu não faria da mesma forma e sempre constato que ia considerar outros pontos, comprovando a evolução do meu conhecimento.

Self assessment quizzes can be a game changer when it comes to finding gaps in your own skillsets. I have a habit of taking regular quizzes to ensure that I have a holistic understanding of the skills and tools. There are a ton of online materials and quizzes available to take.

AI portfolio projects demonstrate skills and interests. They can be made with open-source data, tools, or tutorials. Portfolio projects improve problem-solving and impress employers.

When I'm not working on it professionally, I use AI for fun! It's a great way to take the pressure off and I often use what I learn later with clients. Right now, I'm building out an entire fiction youtube video with AI art, video, and voice to prove out that AI-generated content can have real entertainment value. Another way I keep up with my AI skills is sort of a visual skill tree. If I add a new tool or skill, I add it to the tree. If I see something I don't know or understand, I add it but make it gray until I learn. This is a cool way to keep track of my own personal abilities.

Learning through practical projects shows commitment and skills. Yet, it's crucial to choose projects that align with your goals, be it understanding concepts, comparing AI development tools, evaluating AI models, or solving real-life problems with prototypes. Remember, individual projects have inherent resource limits and might not always lead to marketable ideas. If possible, try to participate in and contribute to a team project or open-source AI repositories. Always address the 'why' before spending time on a project, and allocate your time strategically to the AI projects most aligned with your career aspirations.

To make the most of portfolio projects, I approach them with a few strategies: --Address Real-World Problems: I focus on projects that solve real-world challenges, applying AI techniques to practical scenarios. This not only demonstrates problem-solving skills but also showcases the ability to apply AI in meaningful ways. --Leverage Open-Source Resources: Utilizing open-source datasets and tools allows for the development of diverse projects without significant resource constraints. It also aligns with the collaborative spirit of the AI community. --Document Learning Process: Each project in my portfolio is accompanied by documentation that outlines the learning process, challenges faced, and how they were overcome.

AI side projects are fun. They are also a great way to learn and stay current with new tools and frameworks. If you have the privilege of flexibility at your workplace, consider taking the initiative to suggest creating a small prototype using new tools and frameworks to address a specific business need. Alternatively, allocating time each week to focus on your personal side project can stimulate your creativity and build your resume.

Online competitions like Kaggle, Codalab, and DrivenData can enable AI enthusiasts measure skill improvement, improve performance, and get recognized and rewarded.

This is similar to another question in another AI Question thread. I think the same holds true, those places are great but they usually involve work you'd get paid for. Create your own AI product, seek out expert feedback, iterate upon it, then compete for the ultimate prize: business.

Another strategy I employ to monitor my AI skill progression is actively engaging in online AI competitions. These competitions, found on platforms like Kaggle or DrivenData, present real-world problems requiring innovative AI solutions. For example, I recently participated in a Kaggle competition focused on predictive modeling for healthcare data, which pushed me to refine my machine learning techniques and data analysis skills. Engaging in these competitions not only challenges me to apply my knowledge in practical scenarios but also allows me to learn from the diverse approaches of other participants. This continuous involvement not only benchmarks my skills against a global standard.

Competitions are a great way to track your AI progress. I remember some of the ones I participated in back in 2020 during Covid. I gained experience building chatbots, conversational experiences and natural language processing just by signing up for a few hackathons. Not only did the hackathons provide a way to gauge skill development, it also provided a channel for networking with other AI enthusiasts.

This is one of the most effective strategy to track and improve your skillset in my opinion. Kaggle and other platforms offer good competitive environment for everyone to come together and contribute. By participating in these, you get to learn a lot of new skills and also improve your current skills. It also helps to create connect with other people in this industry.

Another crucial aspect in my opinion. Some things I do regularly to continue development: --Active Participation: I regularly contribute to discussions, post queries, and share insights on these platforms, fostering a two-way exchange of knowledge and feedback. --Constructive Criticism: Receiving feedback on AI projects or concepts helps identify areas for improvement and new learning avenues. This feedback, whether it's on a complex AI model or a basic concept, provides a fresh perspective that is essential for growth. --Advice and Support: These communities serve as a support system, offering advice and encouragement, which is particularly beneficial when facing challenging AI problems or learning new technologies.

By participating in AI-focused online communities, such as those on Reddit or Stack Overflow, I actively engage with other enthusiasts and experts to discuss various AI topics and projects. For instance, I recently shared a machine learning model I developed on a Discord AI group and received insightful feedback that helped me optimize its performance. This process of sharing work and receiving critiques not only broadens my understanding but also exposes me to diverse perspectives and techniques in the field. Engaging in these communities fosters a collaborative learning environment, where I can both offer and receive advice, ultimately leading to a more rounded and informed approach to AI development.

Peer Feedback: Engaging with AI-focused online communities is key for skill development. By participating in these groups, you can: 1- Discover New Approaches: Peers can provide different perspectives and solutions, enhancing your understanding. 2- Get Specific Advice: Community members offer advice based on their own experiences, useful for tackling particular issues. 3- Improve Your Projects: Feedback helps identify and refine aspects of your AI projects. 4- Expand Your Network: Regular interaction aids in building valuable professional connections within the AI field. This method not only boosts learning but also fosters valuable professional relationships.

Emphasizing peer feedback in tracking your AI skill development adds a valuable social dimension to your learning process. Engage in collaborative AI projects with peers. Seek feedback on your contributions, code, and overall approach. Practice pair programming with peers who have complementary skills. This interactive approach allows for real-time feedback, collaborative problem-solving, and mutual learning. Join or create AI study groups where peers can collectively learn and provide feedback to each other. Discuss concepts, share resources, and review each other's work. Incorporating peer feedback into our AI skill development not only provides valuable insights but also fosters a sense of community and collaboration.

Absolutely, I agree on the importance of peer feedback in tracking and enhancing AI skill development. As you provide feedback to others, you also reinforce your own learning. Also, when teaching concepts to peers is one of the best ways to deepen your own understanding.

Mentorship programs are a cornerstone in tracking and enhancing AI skill development. They provide a structured and personalised learning path unlike self-study or generic courses. A mentor, being an experienced professional, can offer tailored guidance, helping you navigate specific challenges and set realistic, achievable goals. This one-on-one interaction ensures learning is relevant, focused, and aligned with industry standards. Additionally, maintaining a project portfolio and engaging in AI communities supplements this learning.

Finding a good mentor can help accelerate your growth, providing a platform to engage in conversations about the latest AI advancements and their implications for both society and the industry. A skilled mentor can also direct you towards resources and opportunities you may not have previously known about. At the same time, if you already have some experience in the field of AI, consider the substantial benefits of stepping into the role of a mentor for those less experienced in the AI field. Mentoring and teaching can serve as a wonderful means to identify your own knowledge gaps and motivate you to revisit AI tools and concepts that may have been dormant in your skillset.

I do everything I can to keep in touch with former instructors or contacts in AI that have helped me along the way. The programs the prompt speaks of I have never used for mentorship or guidance per se, but it follows along the same lines for sure. Find those willing to be continuously willing to listen and advise and you will be fine, if not much better.

Mentorship programs are catalysts in the AI learning journey. Personal coaching from experts provides valuable insights, technical help, career advice and networking opportunities. They accelerate your development and open doors to professional opportunities. Invest in a mentorship program for your AI success.

Platforms like Udacity and Coursera offer mentorship opportunities where I can connect with seasoned AI professionals and educators. For example, through a mentorship program on Springboard, I was able to work one-on-one with an AI expert who provided personalized guidance on my projects, helping me refine my algorithms and improve my coding practices. This direct interaction with an experienced mentor not only accelerates my learning curve but also offers tailored advice and feedback that is specifically relevant to my skill level and interests. Such mentorship programs have been instrumental in helping me navigate complex AI concepts, stay updated with industry trends, and make significant strides in my AI development journey.

Many reputable platforms like IBM, Google, and Microsoft offer certification programs that validate specific AI competencies. For instance, I recently completed Google's Professional Certificate in Machine Learning, which not only tested my understanding of key concepts but also provided a tangible acknowledgment of my skills. These certifications act as benchmarks of my proficiency, adding credibility to my expertise and demonstrating my commitment to staying current in the field. Collecting these badges and certificates motivates me to continuously learn and challenge myself, ensuring that my AI skills remain sharp and relevant in an ever-evolving technological landscape.

The future is skill-based learning, growing and hiring. Skill badges and certificates now often offer more accessible and flexible learning options, which are crucial for people who are working, have family commitments, or face other barriers to conventional education. This inclusivity is vital for diversifying the AI workforce.

AI is at a critical juncture and lifelong learning is essential. The field is evolving rapidly, with new breakthroughs and challenges every day. To keep up with the latest techniques, tools and best practices, I supplement my professional experience (leading complex ML and AI projects for Banks and Insurances) with certificates from reputable providers. I choose Microsoft, AWS, Deepmind.ai and EDx and Coursera for their diverse and comprehensive coverage of AI topics. I also follow some thought leaders (with long form podcasts) and read (many) books on AI to deepen my understanding of this emerging technology. Feel free to reach out if you want some recommendations.

There are plenty of online certifications available today as well as skill badges. If you are someone who is interested in learning AI, take some certification courses from coursera, linkedin or any other platform and complete them. Recently, almost everyone is talking about AI and these are something which will help you to stand out from the crowd.

In my experience, completing AI courses provides the opportunity to develop core skills needed to contribute to AI development. In the last few months, I have completed more than 20 skill-based online courses, enhancing my competence in AI. In the ever-evolving world of AI, skill badges and certificates serve as more than just resume boosters; they're potent tools for showcasing dedication and honing one's expertise. These tangible achievements validate commitment to continuous learning, showcasing the ability to master specific skills and navigate complex AI challenges. Earning badges and certificates helps to gain valuable hands-on experience and build a digital portfolio that speaks volumes about the proficiency for AI development.

Many people are tracking their AI skill development wrongly. You need to intentionally build up a skill tree. How to do this? Use a Notion dashboard. Notion can be a great place to dump and park all your knowledge and skills. Moreover, it can take in code blocks, and can be shared publicly with a link. Another suggestion I have is this: your LinkedIn profile. Many people neglect this. In fact, LinkedIn may just be the platform that your recruiter finds you on! So take the time to analyze your profile and optimize it. Include work experience, projects done, certificates earned. You need to pull your maximum weight in the online world. Because your employer may just stalk your LinkedIn and you won't even know it!

Tracking AI skill development involves strategic planning and ongoing evaluation. Start by setting clear goals for the skills you want to acquire, outlining specific milestones. Engage in continuous learning through online courses, workshops, and practical projects. Regularly assess your progress against the predefined goals, seeking feedback from peers or mentors. Utilize online platforms and certifications to validate and showcase your skills. Embrace challenges that push your capabilities, and stay updated on emerging trends. By maintaining a structured approach, regularly reassessing, and adapting to industry changes, you can effectively track and enhance your AI skill development over time.

In my experience you can track your AI skill development by doing the following, 1. Set Personal Goals: Define what you want to achieve. 2. Define Structured Learning: Follow courses and track progress. 3. Execute Hands-on Projects: Log projects and skills learned. 4. Engage Online: Participate in AI communities. 5. Build a Portfolio: Showcase your work. 6. Track Progression: Monitor knowledge and skills. 7. Networking: Connect with professionals for guidance.

📍There is enormous potential in AI communities such as KI-Insights. Not only can knowledge be shared there, but skills and expertise can also be developed through experimentation and discussion. The diversity of perspectives and ideas creates an exciting learning effect and inspires new approaches. By actively participating and sharing your insights, you not only create knowledge, but also valuable connections and the opportunity to take your AI skills to a new level. In such communities, you can put your passion for AI into action and drive your development forward. Use these platforms to make your learning journey more exciting and enriching.

Drawing from my experience, AI product meetup workshop and hackathons have played a pivotal role in advancing my skills as an AI product manager. These events have empowered me with hands-on problem-solving, deepening my technical understanding and pushing the boundaries of innovation. Concurrently, local AI meetups have proven invaluable for networking with industry experts, acquiring diverse insights, and staying abreast of cutting-edge AI trends. Documenting these experiences is crucial, serving as a dynamic record of continuous learning and growth in the ever-evolving AI landscape. 🚀