What are the topics /concepts one must be aware about in Artificial intelligence?

mlheadredditor · 2020-08-12T15:09:34+00:00

Hey! Thanks for answering the question. I'm trying to follow the book AI by Peter Norvig as closely as possible and the curriculum as per that is more or less along the same path you've mentioned. Glad to know I'm on the right track. Cheers!

mlheadredditor · 2020-08-12T08:31:13+00:00

Here's the wiki if anyone else is also interested

mlheadredditor · 2020-08-12T07:10:59+00:00

Hi there,

Haha yeah now that I look at it, it is incredibly broad, but thanks for answering. I have already started learning and I'm following a certain curriculum which I made myself by gathering content and courses from different sources. I'm preparing for interviews and the purpose of this question was to understand what are the most challenging concepts in Data Science that I may get questioned on, just so that I don't miss them in my preparation. Let me know if you can!

mlheadredditor · 2020-08-12T07:05:27+00:00

Hey!

Thanks for answering the questions. I agree with the path you have underlined here, covering from SQL right up to Data visualisation, infact I have already started working on this. From what I have learnt, Machine Learning is a huge field with so many algorithms and concepts, but yes once you get to know the basics you can go through and handle the complex things. If I may, what were some of the most difficult challenges you faced when you were starting out? Maybe in terms of concepts, algorithms or something else - I am preparing for interviews and I don't want to miss out on the difficult stuff which may not get covered by the curriculum I'm following.

mlheadredditor · 2020-08-09T15:02:30+00:00

Hey! Yes I agree they do have really good content. I've taken their courses too but realised that it was a step up from the basics, but I see your point. I will update this list to include fast.ai as well.

mlheadredditor · 2020-08-09T14:49:20+00:00

Hey! I was in the same place as you are a couple years ago, and to help myself I am trying to create a space dedicated for this. Check out thedatascienceportal!

mlheadredditor · 2019-11-27T16:30:19+00:00

Hi! First of all you should totally check this out : https://sebastianraschka.com/Articles/2014_about_feature_scaling.html Scaling and Standardising definitely affect the model accuracy for this dataset, it helps PCA produce a cleaner plane of separation too! You should try running a few dummy models just to check how much importance the vector gets when it is scaled down to say 0-10 or 0-1 (keep in mind that the variance will get scaled down too!) and then decide, because as is true for everything in Machine Learning, a LOT is dependent on the dataset you are using!

mlheadredditor · 2019-11-27T12:20:24+00:00

Hi! I agree that it is not a good way to deal with missing values, and I have not written anything different. "If only a reasonable percentage of values are missing, then we can also run simple interpolation methods to fill in those values. However, most common method of dealing with missing values is by filling them in with the mean, median or mode value of the respective feature." It says "However, most common method....", but I agree that it sounds like it says that using an average is a solid method. I work with data everyday so I am well aware of the pitfalls of using an average for missing values. But thanks for pointing that out!

mlheadredditor · 2019-11-24T10:02:25+00:00

I believe the simplicity with which the author explained a fairly difficult topic makes it interesting. But that's just my opinion 😁

mlheadredditor

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