1. Black Box Machine Learning
[ad_1]
With the abundance of well-documented machine learning (ML) libraries, it’s fairly straightforward for a programmer to “do” ML, without any understanding of how things are working. And we encourage this “black boxes” machine learning! (At least to start.) However, to make proper use of these ML libraries, one needs to be conversant in the basic vocabulary, concepts, and workflows that underlie ML. We’ll introduce the standard ML problem types (classification and regression) and discuss prediction functions, feature extraction, learning algorithms, performance evalution, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.
This lecture may be safely skipped by those already familiar with practical machine learning.
Access the full course at https://bloom.bg/2ui2T4q
Source
[ad_2]
this is gold for ML starters.
They are using Windows 7?
These are some of the greatest video series on ML.
the indian dude sit in back with glass fking ask too much irrelevant questions and himself not even clear. fking wasting time
Amazing lecture, thanks Bloomberg!
This entire series is the best gift Bloomberg has given to aspiring data scientist!!!
I filled out the form 4 days ago and haven't received any notifications yet to join the piazza forums, is it normal to take this long?
Very nice lecture! Much time spend in Q&As throughout the lectures, never liked that, but that's my personal opinion. Thanks!
I watched this guy's lagrangian duality video in this series and it was the best
I am writing it here because comments are disabled there.
Thanks so much
Great video series! Thanks so much.
Thanks for making these lectures available!
Great Lecture, Thanks