Hello DeepLearning.AI Community! I hope everyone is having a great time!
I need some career counseling. My problem is that I am so confused. What I mean is that the field of AI is growing so rapidly that I am afraid to stay behind. Even though I have completed DL Specialization until now, I still feel like I have a lot of foundational knowledge to cover. Furthermore, I have noticed that I am not able to generate any ideas on which I can implement the knowledge and make my own projects. I do not have proper guidance or roadmap. I need a mentor who can guide me to the right roadmap and keep me accountable and actually help me get the skills that are required in the field.
I am sure that this community has a lot of experts of the field who can guide me to right path and share with me their experiences which can help me land a career in this field. I am also hopeful that this post will also help a lot of other learners like me.
Thank You for reading this post. I am sure the replies will be very beneficial.
My 2 cents: The best way to improve your domain in this field is by doing. I don’t see any way around it.
And to start ‘doing’, I would like to recommend 2 options:
Option 1: Kaggle.com. Get into kaggle.com and engage with one of the competitions. Start with an easy one. You will learn a lot, not only from attempting to create a solution but also, and specially, from others.
Option 2: Omdena.com . Register and apply to one of the projects. Again, pick one that you fill is at this point within your reach. You may or may not be selected to one of the Omdena projects. It takes a few weeks before they tell you. But when you get in one of these projects, you’ll have again an opportunity to practice and develop your skills, and to learn from other while doing.
Kaggle is an immediate step. Omdena may take longer.
Once you get your hand more ‘dirty’, may be ideas will start to flow for your own projects.
Note: I wrote the above from my own experience. Right now I am enganged in on project in Kaggle and another project in Omdena, and it has made an incredible impact in my domain of the field.
What do you think?
in addition to @Juan_Olano‘s great advice:
I am convinced it’s all about the right use case. Often when people see some first positive results on their use case, my experience is that suddenly plenty of potential ways to improve your solution pop up, be it: tuning the model or advancing the data or automatising the system etc.
I would rather spend a little more effort for carefully evaluating your first pilot project that you want to start - maybe it’s the Kaggle challenge that is of highest interest to you, but maybe you also want to search something very close to your job-specific challenges (e.g. from time series domain or computer vision or embedded AI on the edge trained centrally in the cloud…) where you see that this project is a strategic fit and which also might help you to keep your motivation high also for the months and years to come.
Actually some recipes that might be worth considering can be found here:
Hope that helps.