Would you recommend additional literature covering the scope of this Course?

Machine Learning Specialisation - Course 1

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)


Hello @neural_ghost
You can go through the below post to get additional resources for your course


I would recommend the book used in many Universities.
The Elements of Statistical Learning, Data Mining, Inference, and Prediction by Hastie, Tibshirani, Friedman


Hi @neural_ghost !

Thanks for asking! :partying_face:

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.

Link to the post: Aman Chadha on LinkedIn: #artificialintelligence #machinelearning #ai #ml #transformers… | 27 comments

Read List: Aman's AI Journal • Read List

Watch List: Aman's AI Journal • Watch List

Hope you enjoy it! :smile:

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