Machine Learning Etc. Starter Guide
Curious about Machine Learning, but not sure where to start learning?
Start Here: Python for Data Science and Machine Learning Bootcamp
If you only have time for one pointer, follow the link above. If you have time to read two pages of explanation, read on. Either way, good luck. You can do it!
Accessible to Anyone
Most exciting about this era of Machine Learning Etc. is that it is broadly accessible to anyone, and becoming even more accessible as better tools come out regularly. If you meet a troll who says that you can’t do it, brush them aside and do it. You can do it.
Find a Balance: Bigger-Picture, Using-Models, & Background-Study
You can find your own path and balance between areas of study, despite noise generated by partisans and trolls. Machine Learning Etc. is a wonderful diverse interdisciplinary coming-together of people from different areas (journalists, biologists, politicians, mathematicians, roboticists, businesses, etc.). We complement each other as our paths intersect.
1. Bigger-Picture: History, Future, Ethics, Policy; Books!
2. Using Models: popular hands-on resources are…
3. Background Study:
- Statistics & Probability
- Linear Algebra
- Workflow
- History, Computer Science & Related Science (Biology, Robotics, Neurons, etc.)
- Best Practice: Six Sigma, Lean, Agile, SCRUM
- Scientific method, hypothesis testing, significance, causality, randomness, etc.
Workflow
One topic that is rarely discussed clearly in its own right (which is odd) is workflow. Here is a work-in-progress outline I wrote attempting to make a kind of work-flow checklist for projects.
Where to Start
For a practical accessible start to studying Machine Learning, I would use this Python Machine Learning course. It is very clear and well made and covers a lot of useful topics. It covers topics in a clear and focused way and with sufficient detail and practical-use exercises to be useful.
Start Here: Python for Data Science and Machine Learning Bootcamp
Here are other Machine Learning courses by the same person if you want more, or want to start in a different place:
Big Tool
Here is a big overview Data Science Study Tool guide that I am making for other DS students, but it may be overwhelming to start with. It contains lots of overall curriculum information, books for reading, interview prep, etc.
Also it is a work-in-progress.
+ Khan Academy
Khan Academy is a great and very solid general resource if a question leads you their way. E.g. Gradually studying their probability course, among others, is just great.
