I wholeheartedly recommend the fast.ai  course. It provides a lot of instantly applicable code, coupled with very good explanations which you can try out on novel problems later. It's focused on "learning by doing", and not "learning by reading" which fits my style really well.
That said, it doesn't dissuade the watcher from reading later, it's just not recommended to start out with.
I dislike learning from video (upping playback speed helps), I dislike the coding style of the library and the notebooks (nonlinear notebook execution especially), and I still think this is the best available class on anything deep-learning related, and it's only getting better. The top-down, practice-before-theory approach is excellent, but they still get into the theory, often in a much more intuitive and better motivated way than you get elsewhere. Also tons of little breadcrumbs dropped throughout lessons and in the forum to dig deeper for those inclined to.
If you go this route, make sure to follow the suggestion of re-implementing each lesson, from scratch, without referring back to the original notebook. It's a little too easy to not do that and miss out on the lessons you learn from struggling through the actual code.
Thank you for that suggestion.
I would also recommend going through the scikit-learn documentation. Some of the tutorials/examples there are pretty good.
At the end of the day, it all comes down to your personal learning style. For me the thing that worked was to go through the above mentioned steps and then find a problem I was interested in and try to solve it using my newly found skills. That way you will discover new tools and methods.
Finally, the Deep Learning  book is also very good but I would not recommend it to a beginner. It's better to use it when you have a basic understanding of Machine Learning and you want to gain a deeper understanding of the concepts.
Also, see the Gilbert Strang video series on Linear Algebra:
and the amazing 3blue1brown "Essence of Linear Algebra" series:
First one is a bit older school, but takes you through all the fundamentals and actually explains a lot of the math involved. It also gets you thinking a lot more about how to solve problems from a Linear Algebra standpoint and the types of problems machine learning is good for tackling.
Second one is a much more modern day set of courses specifically focused on Deep Learning techniques and problem solving.
I thought both were great. First one is free as well...
I have added a couple of issues to the repo that have been my historic "blockers" towards exploring deep learning: data sets and computation images (docker images, AMIs, etc).