1. Detailed roadmaps for a beginner
2. Prerequisites and resources for every topic.
3. How you taught yourself Machine Learning.
Check out my ML and DataScience CheatSheets here: https://tomer-ben-david.github.io/datascience-cheatsheet
I have some ML introductory lectures on my YouTube Channel, https://www.youtube.com/channel/UC82zocd7ZWMSHe5uuPT4gSw?vie...
I try to keep all material concise for a clean slate learner.
The only exception would be if you're an employee (programmer) of a large firm that's willing to train you and put you in a position to use your skills. But if that was the case you wouldn't be here. Don't spend months of your time self-training because nobody will hire you without hard qualifications.
Also ML is a very large and diverse field, with many different sub-categories. What you learn from online courses depends on the course. Most of them are essentially just training videos that teach programmers how to use a certain library.
If you really want to learn ML, browse for graduate programmes in universities you can attend. If you don't have an undergraduate degree, go get one. If you only want to learn ML as a hobby with no prospects of getting employed, try studying from various online courses (ie on MIT or coursera etc).
Its still evolving, but the earlier parts are pretty comprehensive and resources have been over a year in curation.
It assumes you have a working knowledge of probability, linear algebra, statistics and algorithms at the undergrad level but the recitations (also open) are designed to fill these gaps. From there you would start going through the latest journals/papers in ML. There is also a practical data science class that's open with some ML content http://www.datasciencecourse.org/lectures/
If you can get the entire playlists from youtube before you start watching because often these resources disappear
I've also worked my way through "Hands on machine learning with scikit learn and tensorflow" by Geron and have found it pretty approachable, up until tensorflow anyway.
But https://github.com/aymericdamien/TensorFlow-Examples helped with that.
If so, are you really interested in ML or do you just think its the hot bandwagon of the moment which you want to jump on to get ahead? If that is the case, I'd suggest that perhaps that is a bit obvious and to identify something else that is less hyped and mainstream. Perhaps something which you can get ahead of the crowd on and ideally, have genuine interest in.