Is there a course / article / book where a hollow algorithm for mastering machine learning is given?

Question

Machine learning

At best, I find a list of a million books and courses. At worst, they offer one, MAGIC course, which, according to the authors, will teach you everything, from scratch, in N hours. There is another option, which states that without a math faq, you should not even take it. Is it so?
Books on the topic are like instructions for drawing an owl. First water, then immediately bam (!) And formulas. And the author behaves as if he writes obvious things.
The question is whether there is a source where the necessary preliminary knowledge is indicated as specifically as possible. Exactly, point by point. Maybe there are courses/people/books/religious denominations that give exactly this preliminary knowledge? With a focus on machine learning.
That is, for example, not just - "Linear Algebra", but real topics, with real explanations, with real tests that you need to pass in order to win this linear algebra. Etc.
Thank you.

Answer 1

1. Basic Python course (If you have never programmed at all, start with Learn Python the Hard Way -> Python Crash Course by Eric Matthes -> Automate Everything -> John Zelle "Python Programming"
(required(!) to do all the exercises - to get hands on, because only the material will really be mastered) (if you already have
experience in programming: Allen Downey - Think Python, Diving into Python and Learning Python (Lutz))
-less professional programming: The best and most detailed book on Python algorithms IMHO: "Data Structures and Algorithms in Python" by Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser
1B: there is a Lucid Programming channel on yotube, where the author also shows a lot of algorithms using examples or Udemy, where I took Calculus classes from Krista King, I liked the way she explains)
There are good videos from Imperial College of London on mathematics:
Mathematics for Machine Learning full Course || Linear Algebra || Part-1 https://youtu.be/T3TpdPmTLso
Mathematics for Machine Learning Full Course || Multivariate Calculus || Part -2 - https://youtu.be/m998PdOCFcY
3. Further free ML course from Andrew Ng on YouTube and Courser - free
3.A Also a very detailed course in mathematics from the creators of DS for R: An Introduction to Statistical Learning - University of Southern California - available for free - where all the ins and outs of mathematics are shown in great detail
4. Hands-on machine learning with scikit-learn and tensorflow - o'reilly - a lot of practice and theory
5. Python for Finance - O'Reilly - in general, O'Reilly on Python has very good books on python and ML, where you can see a lot of things in action
6. a bunch of courses for $10 on Udemy by Jose Portilla (including R, SQL, Spark with Python, Spark with Scala, Computer Vision, NLP, Plotly, Algorithms, Python for Finance, Deep Learning: TensorFlow, Keras etc etc etc, ) - if you take courses on udemy and shows more than $10, write a comment - I will give you a promo code for the cat. 94% discount - the code is given to those who have already bought his courses)
7. Siraj Raval has a very cool YouTube channel on Data Science - a lot of examples, videos, information, etc. (find a video there on how to learn Data Science in 3 months, there are also a lot of resources , but IMHO for 3 months, as he says, is unrealistic)
8. "data science from scratch by joel grus" old book also from O'Reilly
in fact, I would advise starting with it, even if there is no experience at all and nothing is clear at all, because it gives the whole plan of what you need to learn and how to organize the whole process for yourself (and then periodically return to it, check with the plan)
There is also a very similar book Python for Data Analysis Book by Wes McKinney (creator of pandas), but a little simpler, and I would still advise them to read both together, tk. this one is much more focused on data cleaning
In general, Data Science is actually not a very difficult discipline, in fact, you just really need to know a lot in order to really start doing something there, i.e. a large threshold for entry, but the algorithms themselves are very, very easy to write and work with, etc. etc. It is more difficult to prepare a date, model it, somehow try to give up on it in order to start working, and the process itself and the python code are as easy as shelling pears.
Well, as you were told above, how to master all of the above (hahaha), welcome after all this on kaggle to start doing it all in practice;)

Answer 2

Is it so?

I'll tell you a smart thing, but just don't be offended .
There is no hollow algorithm. That is, it is, but does not work. I mean, it won't work for you. Guys like Euler or Bernoulli beat linear algebra with no real topics, no explanations, no tests, but you're not one of those guys.

Answer 3

it is possible and triangles for several years