New answers tagged

1

I worked as a data modeler at Siebel. One of the books that the Siebel data model is based upon is Data Model Patterns by David C. Hay. The book will not teach you how to build great data models. It is a collection of robust data models that are used in big systems used world wide. The one book that I have used (in teaching) that focusses solely on data ...


2

It's hard to know what you really know from just your assertion that you've "learned to code and know the basics of Machine learning as well." Do you mean that you can write a script/notebook in Python/R using libraries like sklearn or tensorflow? Can you do a full ML pipeline - preprocess data, train a model, then test and evaluate it's performance (oh, and ...


3

The first two paragraphs of the Wikipedia article on Machine Learning will give you a good outline of the things you need to know. Algorithms and Data Structures are key from CS, but you also need quite a lot of things like Statistics, Mathematics, Pattern Recognition and such. It is a pretty long road if you start out only knowing how to program. It is ...


2

The best approach to learning just about everything is twofold. You need to practice and you need feedback on your attempt. You learn by reinforcement, not by seeing something once. This is why coursework depends on student exercises, not just lectures and textbooks. So, to learn a new thing, I suggest that you first get an overview idea of it, say from ...


0

[From you comment] P.S need such combinatorics after which I can understand the course of data structures and algorithms. Combinatorics don't factor into data structures and algorithms in this way. If your goal is to understand data structures and algorithms, combinatorics are an irrelevant topic. Combinatorics is also quite vague, as this can effectively ...


Top 50 recent answers are included