May 29, 2021 Article blog
I'm sure everyone knows that Python is inextricably linked to AI, and Python has a huge advantage in doing artificial intelligence.
The two most important program languages in data science and machine learning are Python and R, which are easy to learn, have a wide range of applications (not limited to data analysis) and have a smooth learning curve, making them the first programming languages to get started, and can perform data analysis through pandas, SciPy/NumPy, sckikit-learn, matpllib, and statsmodels for engineering tasks and projects that require integration with network applications. A s for R because it is a programming language developed by statisticians, it is good at statistical analysis, charting, often used in academic research fields, it is recommended to have a certain degree of mastery. I n general, Python and R are not mutually exclusive, but complementary, and many data engineers and scientists tend to convert in both Python and R languages, using R for small-scale model validation, statistical analysis, and charting, and moving to Python when writing algorithms and databases, network service interactions, and so on. In order to reduce the cost of learning.
In addition, Python itself is a common language, in addition to data science can also be widely used in network development, website construction, game development, network crawlers and other fields, when you need to integrate system products and services, you can serve as a one-stop development language, and more importantly, Python can also be used as a glue language is very easy and C / C , and other more efficient language integration. In short, Python is a simple but powerful program language worth investing in, so let's start with Python.
For Python and R comparisons, there are two articles here that refer to the data science community's Huashan Sword: R vs. Python Peak, By is for better data analysis: R or Python?.
In fact, data science is a cross-cutting discipline, and the following knowledge is often required in learning how to use Python for machine learning:
In order to master the above three areas of knowledge (we first focus on the core techniques of machine learning, temporarily ignore the data science of the knowledge of the field), specifically, we can have the following steps to refer to:
It is recommended to install Anaconda, which supports multiple versions of Python across platforms, with a suite of data analysis and scientific calculations installed by default, with the spyder editor, Jupyter Notebook, which provides a web version interface that allows users to develop and maintain Julia, Python, or R programs through their browsers.
MachineLearning: Python Machine Learning: Use Python
Reprinted from: https://www.php.cn/python-tutorials-459063.html
These are the whole contents of the small editor's study python to do artificial intelligence.