Would you have some books, blogs or papers to recommend on Course 1 scope? I’m mostly interested in calculus and parallel computing.
Or perhaps it’s better to finish Course 2 and possibly Course 3 to ask this question?
For Course 2 that covers Linear regression, Logistic regression, Decision trees, RandomForest, KMeans, and Neural Networks - my question would be: would you recommend some resources on the algorithms not covered in this course, such as SGD, SVM, KNN, and NaiveBayes?
(mentioned in this related question: Coverage of ML algorithms in the ML specialization course)
I would recommend the book used in many Universities.
The Elements of Statistical Learning, Data Mining, Inference, and Prediction by Hastie, Tibshirani, Friedman
Yeah, I think there’re quite a lot of open-source courses/textbooks on the Internet nowadays. Previously, I encountered a LinkedIn post which gave a great summary of the available resources.