How do you learn new machine learning features?

Learning new machine learning features is critical in ever changing Technology landscape and provide great solutions to your customers or your own features.everyone journey is a unique, so please be aware of your goals . Your goals can be specific and to achieve that . 1. Continuous learning mindset 2. Look out for international ML conference to share information 3. Online webinar, seminars 4. Engineering blogs, research papers 5. Follow the ML leaders in LinkedIn

Identify your goals and then keep working on it everyday. You will see after some weeks that you are doing great and learning quickly and getting some good skills using different tools and methods. Always keep working on the same goal so you will not be wasting time by going here and there.

Learning new machine learning features necessitates ongoing learning and practical application. You can begin by pinpointing your objectives to streamline your focus and resources, aligning them with relevant, multifarious resources like online platforms and scholarly articles. Practical assimilation through projects and challenges enhances understanding and skill. Engage with the community via online forums and conferences, sharing insights and gaining diverse perspectives. Combining focused learning with diverse, practical experiences and communal interaction, this holistic approach fosters a profound, versatile understanding of evolving machine learning features.

Learning is continuous process whether it is ML or anything. To learn specific to new features I’ll be following published papers, read docs/ API docs. And also following the SME’s in the space, contributors /community channels to get the updates. Hopefully all those give some perspective to start. Then more practical learning achievable by applying it to different use cases.

Finding reliable and relevant sources for your ML studies can be daunting, because much of what is available is either just marketing material or quickly go out-of-date. Here are a few guidelines that I've shared with my students in the past. 1. For the basics, go to the classics. While the frameworks may change, the underlying concepts do not - and if you understand the theory, you'll quickly know how to apply whatever new arises next week. 2. For the advanced, find respected curators that are actual AI engineers or researchers, and not some AI guru with a marketing or MBA background. There are many respected curators that filter the signal from the noise with regards to latest research, and make it highly accessible to understand.

While many sources provide theoretical knowledge, it's equally important to seek out resources that offer hands-on, practical examples. Interactive tutorials and workshops can provide a more immersive learning experience. Additionally, joining machine learning communities or groups can provide a platform to discuss and validate the authenticity and relevance of various sources.

Incorporate varied learning methods. Delve into scientific papers, reference blogs, and newsletters to stay abreast of emerging trends and theories. This theoretical groundwork enhances your understanding and foresight in the field. Utilize your growing expertise to unearth and assess potentially impactful paths in machine learning. Engage in critical discussions, forums, or seminars. Sharing and juxtaposing your insights with peers can unveil new perspectives and opportunities, fostering a holistic learning environment and nurturing a more profound grasp of machine learning intricacies.

Source control is a great way to keep track of your AI projects. Tools like GitHub allow you to track both your code as well as learnings in blog posts. There are many similar systems to choose from. It is recommended that you keep track of your mastery of different AI techniques by completing projects. These build up your skills and provide a reference point for future implementations. First prize is to do these in your own job, but if you don't have the opportunity then online environments such as Kaggle provide a fun way to learn as well as to store notebooks and receive feedback from others on improvements. Good luck with your learning journey!

Frank Zappa said that: “Without deviation from the norm, progress is not possible.” The same goes for AI. In order to progress one has to break away from the existing and experiment with the new. This is more akin to the field of engineering than of science, where you try to explain what you observe. The current state of AI is one where there is continual building on existing frameworks by means of exploration, often without quite understanding why things work. The scientists can figure that out later. You don't need to understand something in order for it to be useful. Look at LLMs for instance. There is much we don't understand about them and yet they are driving innovation at pace.

Data science is a team sport. Teams that cannot collaborate effectively cannot succeed. What better way to collaborate than to learn together. There is always something new to learn. Discussing it with team members or sharing it on forums are all ways to progress together. Find your niche and get stuck in. There is no such thing as a silly question. Ask if you don't know. It is also hard however, as time is usually limited, so spend your time wisely. Don't waste your energy on trite comments and content to boost your influencer value. Research and contribute meaningfully. If you have nothing to say, then rather say nothing.