@raybard , You are right that the MLS is not about teaching coding, but does offer example codes for you to study them yourself. I understand that you want to be able to code from scratch, but I also need you to figure out for you what are the missing puzzle pieces among the following:
- Know Python
- Know Data Science Libraries (numpy, matplotlib, tensorflow, and so on)
- Know ML concepts
- Know how to translate ML concepts into code
- Know how to debug your work
- Others? Can you describe it in one short sentence?
For 1 & 2, I suggest you to check out this post. For 3, this is why you want to take this MLS, or other courses like the Deep Learning Specialization that cover the topics of your interests.
For 4 &5, and actually 1 & 2 as well, this is about practicing. You may start with asking yourself how many ML projects (be it tiny, small, other scale) are you doing right now? How many hours do you code per week? If you are lack of ML project ideas, you may search the coursera catelogue - they offer a “guided project” filter - for some projects; or you may visit Kaggle, go to the “competition” page where you will find categories for beginners such as “Getting Started” and “Playground”.
Some places, like MLS, provide 100% ready codes (in optional lab) or less than 100% (in assignment) for you, but have you tried to -
- organize, on a piece of paper, the concepts covered by a lab.
- select 1 to all concepts, open a blank notebook, and implement it yourself, reference back to the lab notebook if needed. Note that you need to distinguish between codes that’s about doing ML, and codes that’s for the logistic of this course - like tests, load_data and etc. Logistic stuff is unimportant and you may just copy them instead of implementing them yourself. Don’t distract yourself with those logistic stuff from the main dishes.
Lastly, I suggest you to start coding today. I see quite some people trying to “find the best way to learn” without actually coding, and they could have wasted months of time with 0 actual progress. To me, the best way doesn’t need to be found, instead start coding today by setting a goal - be it a guided project, a kaggle, or repeating optional lab - and whenever you have a problem, google sample codes or solutions yourself - there could be plenty of answers already available on stackoverflow for exmaple. This process takes months of time and has a very steep learning curve at the beginning, but you will see the pay-off - the experience, the muscle memory to code, the knowledge, and being able to code and debug as an independent programmer.
We are self-learners now.
Raymond