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For fuck's sake,if you are teaching "Machine Learning For Developers",you don't have to waste a whole hour explaining what the fuck a variable is or what is an if statement.Developers know what that is....aaargh.Off to sleep.

Comments
  • 13
    Thats the problem with most online tutorials, lessons, etc. There is always a beginners part, which is like 25% of the course
  • 6
    This is why I study on my own, reading just official documentation, other peoples code, books, and practicing.
  • 4
  • 13
    I have three words for you.

    Washing Machine Learning
  • 6
    You'd be surprised. I'm on my final semester of CS Engineer, and 80% of the class don't know what's a variable, what's a field, what kind of memory management Java uses.

    If that wasn't enough, they think that the variables in Haskell are the same that in Java...
  • 5
    Ok now people tell us which maching learning courses you suggest
  • 2
    Following.
  • 4
    I'd advise to rely on books and papers.I'll post my list here soon.The only online course i find worth it is Andrew Ngs on Cousera/Stanford Engineering.
    Most courses are totally useless and only focus on Tensorflow and other libraries (Udacity courses in particular).Please don't use any material that uses only frameworks, that's why i suggest books.
  • 1
    @apex haskell doesn't have variables since they can't be modified.

    BTW I'm on my sixth(out of eight) semester and I think most of my classmates know what a variable is lol.
  • 2
    @Luigi003 Sorry to burst your bubble, but Haskell DOES have variables, it's just that the context changes, because Haskell being a functional programming language it takes math concept of a variable, that's that "It's a variable that can take any value, but during evaluation time, it becomes a constant, until a result is given", or in other words, they're "immutable".

    About my classmates not knowing what a variable is, it's about how some languages handle them (in memory, reference, scope, etc.)
  • 2
    @theScientist Great.I can say confidently now that i have a gist of what machine learning is all about.
    Here is how my schedule is/was:

    - Finished Andrew Ng's course.(cs229.stanford.edu).
    - Took Prof. Yaser's Caltech course on Learning From Data.
    - Read "Building Machine Learning Systems with Python". This is easy once you finished the first two it's more practical and necessary.
    - Now am currently reading two books
    1.Machine Learning: A Probabilistic Perspective
    2.Pattern Recognition and Machine Learning.These are more theoretical and you can't start with them straight away.
    Other materials i read intermittently:
    - Berkley's Deconstructing Data Science : data8.org
    - I am waiting for Dr. Fei Fei Li's CS231n lectures to be put online. : cs231n.github.io

    This might not be comprehensive but it's what is working/worked for me.
    PS.I also got my first Machine Learning freelance job( transcribing YouTube videos).
    Happy reading and see you on the other side of the net.(neural net)😀
  • 0
    Nice! Keen to start learning this. 👌🏼
  • 0
    I learned ML and DL from books and NPTEL. I would definitely suggest mithesh khapras Deep learning from IIT Madras.
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