Im figuring out how to tackle this. Rather than jumping ship into Pytorch and implementing models. I think its best to spend a couple of months on data collection and data processing using python libraries like NumPy, pandas etc. That way i get some proficiency in python which i have some experience in already and i get to understand the first step and crucial step in ML.
Once i'm quiet confident with python data collection and data processing, then it would be a good time proceed to learn PyTorch, learn about various models and how to implement them and training the models.
Essentially the plan is:
- Start with learning data collection and data processing to get proficiency in this step and familiarity with python.
- Proceed to learn PyTorch and implement various ML models.
What do you think of this plan? feel free to suggest anything else.
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