Last time was only able to maintain the Python environment, so This time, we will go further and prepare for machine learning with Python!
... or rather, it will be a reference bookmark memo. Don't even get to the entrance ... Machine learning
The following sites may be useful
-Learning site from introduction to application of Python
However, as a caveat, Python is not compatible in writing between 2 and 3 series. Since the printf is written differently, it may not work quite well depending on the site you refer to.
-Changes from Python 2 to Python 3.0
The following packages seem to be often used in machine learning
NumPy | A library that performs processing such as matrix calculation, various mathematical functions, linear algebra, and Fourier transform. |
SciPy | The core package of scientific computing routines |
scikit-learn | python machine learning(Machine Learning;Machine learning)Module |
matplotlib | Python graphing library |
pandas | Data frame(Tabular data structure convenient for data analysis)Library to create and operate |
Please refer to Last time for how to install.
The following may be useful for learning how to use
While investigating, when writing Python, the IDE's "Jupyter Notebook" Since it was introduced several times, I will post a link that was easy to understand.
-Powerful notepad for modern engineers Jupyter notebook recommendation -How to build a technical notebook environment using Jupyter Notebook -How to start the Jupyter Notebook server
That's it.
If you have any questions, please comment. mm
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