Questions About Data Science from Scratch: First Principles with Python

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I am looking for a book which explains how to write the popular ml algos in python and explains them, should use python 3. Will this do ?

Simon Time is now ripe for using python3 in data science since is is supported by major libraries such as numpy, scipy, pandas, scikit-learn, tensorflow, ma…moreTime is now ripe for using python3 in data science since is is supported by major libraries such as numpy, scipy, pandas, scikit-learn, tensorflow, matplotlib [0].

Unfortunately, this book is based on python 2.7 [1]; that notwithstanding, I *would* recommend this book since it is very well written!

The approach of the author is not to explain how to merely apply the pre-made data science tools (i.e. the aforementioned libraries), but rather he *teaches the actual algorithms* behind the principal ideas in statistical analysis and machine learning by coding them in pure python, which is great!

Now, be aware that in real applications of data science you will most probably not follow this route (that is, coding your algorithms directly in python) due to the slowness of python. The preferred approach is to use python to call highly optimized libraries such as numpy that do the actual computation for you.

For this reason, differences between python 2 and 3 are minimal among the features of the language used in this book.

What I am trying to say is that you can most likely follow the book as if it were a python3 book, and the only issue will be having to change "print x" into "print(x)" as needed.

0: http://py3readiness.org/
1: as stated in chapter 2, paragraph 1 "getting python"(less)

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