What do you do if you're facing roadblocks in your pursuit of Machine Learning knowledge?

I believe in learning by doing, so my natural response is to stress test your knowledge by doing a project and seeing where you are struggling as you build it. This way you not only know what you don't know, but you also know how costly not knowing this might be.

Hitting a wall in your machine learning journey is common! Here's how to break through: Identify the Blocker: Is it a specific concept, a technical hurdle, or a lack of motivation? Pinpoint the issue for targeted solutions. Seek Help: Don't struggle alone. Join online communities, forums, or courses to connect with other learners and experts. Break it Down: Chunk complex topics into smaller, manageable parts. Focus on mastering the basics before diving deeper. Practice Makes Progress: Hands-on experience is key. Work on projects, participate in hackathons, or try online tutorials to solidify your understanding. Refresh & Recharge: Feeling burnt out? Take a break, explore different learning resources, or find a mentor for guidance.

1. Identify challenges. 2. Seek help from mentors and communities. 3. Break down complex concepts. 4. Practice through projects. 5. Explore various learning resources. 6. Review fundamentals if necessary. 7. Take breaks to prevent burnout.

Try identifying and realizing the problem and its level. For example, It could be a subject that you are not paying attention to. For example, is there something like Statistics, or Linear Algebra that breaks your pace? Or is it about your ability to focus, procrastination when it comes to practicing? Is it about getting distracted and deviated from too much there to learn? You need to figure out the problem and its 'dimension'.

Consider whether the roadblocks stem from internal factors such as gaps in your foundational knowledge, lack of clarity in learning objectives, or ineffective study habits, or external factors such as inadequate resources, limited access to mentorship or support, or time constraints. Once you've identified the source of the problem, devise a plan to address it accordingly. This may involve breaking down complex topics into smaller, more manageable chunks, seeking additional resources or guidance from mentors, peers, or online communities, practicing hands-on coding exercises to reinforce learning, or establishing a structured study routine to stay organized and focused.

Join online communities, forums, or courses for feedback and guidance. Participate in projects, competitions, or hackathons for learning opportunities and constructive criticism. Feedback helps identify strengths, weaknesses, and fosters new perspectives, accelerating skill development in machine learning.

Projects, competitions, and hackathons are a great way to get feedback and learn some new approaches in the field. However, what's also important is getting feedback from those who will benefit from anything build with ML. Try making a very small, simple product with what you know and see if people take interest and find it useful. You need this kind of feedback too.

When facing roadblocks in your pursuit of Machine Learning knowledge, seeking feedback and guidance can be invaluable in overcoming challenges and moving forward. Start by reaching out to mentors, peers, or experts in the field who can provide insights, advice, and support tailored to your specific situation. Share your concerns, questions, and areas of difficulty, and be open to receiving constructive feedback and suggestions for improvement. Additionally, consider joining online forums, communities, or study groups dedicated to Machine Learning, where you can engage with like-minded individuals, exchange ideas, and collaborate on learning projects.

In my experience, when I am learning any new concept which seems challenging I would usually go to ChatGPT, ask it to give an analogy for the concept and a example in which it could be implemented, I can say with first hand experience that it infact worked for me, analogies are powerful tools to understand complex topics and especially their usage and implementations. I hope that heped

You may learn a few things better with a book, and other things through watching a nicely animated videos. There are two things to keep. One is consistency. Be hell bent to master. And the other is the ability to pivot. Know before giving up, you have options to change your source. for example, give read to another book, or switch to a different content creator. This is the best thing with this abundance of content. Nothing is difficult to learn anymore once you get the right content for yourself. All you need is that persistence to find the right guy, and ability to pivot when to switch to a different guy.

In general, I look at knowledge mattering only as much as it's useful. Regardless of the format, you should be getting some form of tactical and strategic knowledge. Whether that's from books, formal education, projects, etc., the point is the getting tactical/strategic knowledge that's proven useful, not where you got it.

Start by diversifying your learning materials, including textbooks, online courses, tutorials, research papers, and practical projects. Explore different learning platforms, such as Coursera, Udacity, edX, and Khan Academy, to access a wide range of educational resources tailored to various learning styles and preferences. Additionally, engage with the Machine Learning community through forums, discussion groups, and social media channels to seek advice, share insights, and learn from the experiences of others. Don't hesitate to experiment with different study techniques and approaches, such as hands-on coding exercises, group study sessions, or attending workshops and meetups, to find what works best for you.

I believe that the abundance of content, and this never seen before ease to learn something have weakened our ability to memorize stuff for long! In order to keep things with you, apply what you learn and practice it. Use what you learnt a couple of times and it will stick with you for a long time.

When facing roadblocks in your pursuit of Machine Learning knowledge, one effective strategy is to apply what you learn. Instead of getting stuck in theory or feeling overwhelmed by complex concepts, focus on practical implementation and hands-on experimentation. Choose a specific problem or project related to your learning objectives and apply the techniques and algorithms you've studied to solve it. Break down the problem into smaller, manageable tasks, and gradually build up your understanding and skills through iterative experimentation and refinement. Leverage online resources, tutorials, and community forums to seek guidance and troubleshoot issues as they arise.

Back to what I said about strategic and tactical knowledge, it's not where you got it that's important but how useful it's proven. The only way you can truly prove it is by using it and verifying it's effective. You experiment with what knowledge you have, observe the results, and determine if it was useful or not. Now, I can be a bit of an empiricist, but this is true even if we are talking about something useful in a qualitative sense, in which case you'd probably rely more on simple heuristics than strictly defined metrics.

Always apply what u learn, don't just read or watch the content and leave it there . Make some handy notes for the revising concepts and always learn by practical implementation. Don't copy projects and source code form online sources , use your own common sense and chat gpt for helping to improve ur source code rather than writing code itself.

If you're facing roadblocks in your pursuit of Machine Learning knowledge, maintaining a growth mindset is key to overcoming challenges and continuing your learning journey. Embrace the belief that your abilities and intelligence can be developed through dedication and hard work, rather than being fixed traits. Approach obstacles as opportunities for learning and growth, rather than insurmountable barriers. Seek out resources such as online courses, tutorials, books, and mentorship to expand your knowledge and skills. Break down complex concepts into smaller, more manageable tasks, and celebrate your progress along the way.

Some things that keep you growing in ur professional career are mentioned below: Embracing Challenges Learning from Failure Persistence Seeking Feedback

Finally, know what you have to learn. And what you do not have to learn! One way to figure it out to ask yourself, that ok, I really really want to learn stuff A. Will I get a chance to apply it or use it in the near future, given that I am already committed to do B, C, and D? You will get a No for a lot of stuff. And thats where you need to realize that if you don't practice that skill, it will be wiped out of your mind in a matter of days. So there is no benefit to learn it. I have been stuck through this issue where I wanted to learn everything that glittered on my homepage. The fact is that we have limited time, limited energy, and a limited focus.