3 ms·

I think a lot of people just don't realize that Machine Learning can be as simple as a linear regression with 2 variables. The hype and myth around ML are such

I think a lot of people just don't realize that Machine Learning can be as simple as a linear regression with 2 variables. The hype and myth around ML are such that some employers just assume that they need to hire rockstar data scientists to do ML but in reality, a junior level data analyst or even a non-technical employee can do simple ML model in Excel spreadsheet assuming he/she has some background in Stats 101.

steve19 8y ago
Yes but that is the easy part. The hard part is knowing what can be solved with ML, gathering the training data, validating the model and then explaining what the model does. Acually fitting the model is not the hard part. But yes, not all ML is deep learning and most ML problems should NOT use deep learning.
- enraged_camel 8y ago
  >>The hard part is knowing what can be solved with ML I’d say THIS is the hardest part by far. It requires a very rare mix of business domain expertise and technical knowledge with regards to ML/DL.
- mr_toad 8y ago
  > Acually fitting the model is not the hard part. Ideally you shouldn’t be fitting the model yourself. The idea behind ML is to use brute force computation. If you’re fitting the model by hand, you’re a Statistician.
  - Analog24 8y ago
    Statisticians do not fit models by hand, they "fit" models in the same way machine learning pratictioners "train" models. "Fitting" in statistics lingo is completely synonymous with "training" in ML lingo. Statisticians were the original ML practitioners.
    - steve19 8y ago
      I will second this. I use the two words interchangeably. I would probably use the word fit when talking about liner regression and train when talking about a more complex ML model that requires more computation. I really don't remember the last time I fitted a regression model by hand, it would have probably been in a college exam.

steve19 8y ago

Yes but that is the easy part. The hard part is knowing what can be solved with ML, gathering the training data, validating the model and then explaining what the model does. Acually fitting the model is not the hard part. But yes, not all ML is deep learning and most ML problems should NOT use deep learning.

enraged_camel 8y ago
>>The hard part is knowing what can be solved with ML I’d say THIS is the hardest part by far. It requires a very rare mix of business domain expertise and technical knowledge with regards to ML/DL.
mr_toad 8y ago
> Acually fitting the model is not the hard part. Ideally you shouldn’t be fitting the model yourself. The idea behind ML is to use brute force computation. If you’re fitting the model by hand, you’re a Statistician.
- Analog24 8y ago
  Statisticians do not fit models by hand, they "fit" models in the same way machine learning pratictioners "train" models. "Fitting" in statistics lingo is completely synonymous with "training" in ML lingo. Statisticians were the original ML practitioners.
  - steve19 8y ago
    I will second this. I use the two words interchangeably. I would probably use the word fit when talking about liner regression and train when talking about a more complex ML model that requires more computation. I really don't remember the last time I fitted a regression model by hand, it would have probably been in a college exam.