Principle and programming practice of machine learning algorithm Chapter One basics of machine learning __ Machine learning

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of machine learning!

1.1 Programming language and development environment: Select Python development environment (build process omitted) (of course, also can use R language)

The reason for the selection is as follows:

1. Free, open source

2. Python programming is simpler

3. High efficiency in development and implementation

4. Rich program Library, support vector programming

5.python Support Network programming

(1) Python developed a similar mathematical analysis library in the following matlab: NumPy and SciPy

(2) Realizing the visualization of data with Matplotlib

(3) The most important thing is that Python developed a scikit-learn machine learning algorithm Base

(4) provides the depth learning algorithm library Theano, and supports the GPU computation

Order of installation of Python algorithm libraries: Numpy-scipy-matpltlib-scikit-learn

1.2 IDE selection and configuration (UltraEdit): Small, powerful, support for remote development