02/08/2020 - studying

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  • machine learning is really difficult
  • i'm not fond of theory, i have a hard time visualising theory, i can only grasp it by puttering around and practising
  • math - symbols, formulas, not my favourite. and had a hard time at school with math
  • there's no playground to simulate or test out the algorithms, so this is really dry for me
  • basically my aim to is understand a little of the theory enough to be able to pic algorithms
  • find some good examples with step by step on python like scipy
  • and after that move on the data cleaning etc
  • so far i have been studying with a combination of - o reilly books, youtube, udemy - i change it up
  • books
    1. nlb ebook - data science from scratch - shows under the hood implementation details written in python instead of library methods, might have to skip pages if you don't want to know. does have some example use cases.
    2. nlb ebook - o reilly thoughtful machine learning with python
    3. nlb ebook - o reilly hands on machine learning with scipy and tensor flow
  • youtube
    • youtube/coursera - andrew ng - 
      1. [+]pioneer in the field
      2.  [+]slow paced, explains the math and the parameters, concepts are well done. 
      3. [-] uses octave as the implementation tool of choice which is not mainstream eg python or R
      4. this course is highly recommended by the internet btw. 
      5. [-] i keep drifting off due to lack of pretty visuals, the ugly handwriting, lack of python code. there are graphs and all, like a lecture in university
    • youtube - brandon foltz
      1.  - stats 101 for machine learning, 
      2. warm up for linear regression, the simplest machine learning algorithm. only up to linear regression.
    • youtube/edx - sanjoy dasgupta - 
      1. started me on vectors and gradient descent etc. 
      2. rather technical. doesnt drill down the concepts like andrew ng. soft spoken.
  • udemy 
    • -data science 365 - 
    • doesn't go into many machine learning algorithms, only up to linear regression.
    • includes the stats videos. 
    • some mentions of deep learning and neural network.
    •  i supplemented it with brandon foltz videos. 
    • more like a 101 course. 
    • pretty graphics. code with annotation is available. you can run it on kaggle in case you dont like following code throughs. i like udemy platform, theres transcripts, pretty visuals, note taking, a structured playlist. it feels structured without being stifling.
  • there's also data camp at 25/mo for more structured learning, which has its pros and cons. it has a code editor and you can run your code inside the website. however the teaching materials are less pretty so it might feel like you are just following a code through step by step. 

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