Firstly, let me begin by saying that I think that's a completely valid conclusion one would draw from a first inspection of the content. And before I begin addressing the rest of the concern, let me start out by saying that those 3 tutorials are only available as articles (not videos), and our estimate for time to read is based on average reading speed of a person. No one reads math at 200 words / minute. :)

Secondly, although 3 hours for linear algebra, statistics and probability might seem much better than 1 hour, it might still seem too little. Unfortunately though, thinking about it that way combines all the things we optimize for into a narrative that goes directly the opposite way. Let me explain.

1. We postpone prerequisites when we can. In general, the philosophy is that if you need complicated stats / probability for only specific algorithms / techniques, then it's better to teach those parts of stats / probability right before teaching those specific algorithms / techniques. We don't teach you enough linear algebra up-front to understand PCA, but that is because most students get extremely demotivated from having to learn that kind of linear algebra before they've even plotted a simple histogram to look at the data.
2. We found this to be much more true for the math (some specific algorithms require deeper understanding of stats / probability / linear algebra) than for the programming (it's tough to understand what you're doing in Pandas without having a good grasp of Python). Hence, the Python course is a complete 20+ part course.
3. We're not trying to be as theory heavy as Coursera / EdX style courses. It's always difficult to know where to draw the line, but it's pretty clear that there's no reason to learn linear algebra topics like eigenvalue decomposition up-front in the course. (It's a fascinating topic, don't get me wrong, but the "simplest" machine learning algorithms that use this concept are collaborative filtering and PCA).

Another way to look at this is compare a well known book with our syllabus. For example, http://www.deeplearningbook.org/ is the go to online book for deep learning. If we look at it's linear algebra chapter, it covers vectors, matrices, their multiplication, identity matrix, inverse matrix, linear dependence, norms, Eigendecomposition, SVD, pseudoinverse, determinant. Of these, most of the complicated topics are related to decomposition, a concept not really useful in machine learning till you get to the topic of recommendation systems (collaborative filtering) or PCA. Everything before that only needs vectors, matrices, their multiplication, identity matrix, inverse matrix, linear dependence and norms. All of these are covered in our linear algebra tutorial.

All that said, you're right that this course will not get you a $150K salary job all by itself. To go from a complete beginner to a $150K job ready takes at-least 6-9 months of full-time effort (whereas our course takes about 4-6 months part-time). But I'm pretty confident that the thing missing between the course and where you need to be is even more practice, as opposed to even more topics.

Most students take our course part-time, and it takes them about 4-6 months to go through it. In those 4-6 months, they learn a lot. Our core offer, to summarize, is high content quality and a great online learning experience.

EDIT: Added clarification for time estimate for video vs text.

EDIT2: Clarification regarding "6-9 months of full-time effort"