How python is used for artificial intelligence

How python is used for artificial intelligence: 1. Master basic Python programming language knowledge; 2. Understand basic mathematics, statistics and machine learning basic knowledge; 3. Use Python scientific computing function libraries and suites; 4. , Use [scikit-learn] to learn Python machine learning applications.

【Related learning recommendations: python tutorial】

Python’s approach to artificial intelligence:

Why choose Python?

The two most important programming languages ​​​​in the field of data science and machine learning are Python and R. Python is simple and easy to learn, has a wide range of applications (not limited to data analysis) and has a gentle learning curve, making it suitable for first-time users. It is an introductory programming language that can perform data analysis through pandas, SciPy/NumPy, sckikit-learn, matplotlib and statsmodels. It is suitable for engineering tasks and projects that require integration with network applications. As for R, since it is a programming language developed by statisticians, it is good at statistical analysis and chart drawing, and is often used in academic research fields. It is recommended that you have a certain degree of mastery. Generally speaking, Python and R are not mutually exclusive, but complementary. Many data engineers and scientists often switch between Python and R. They use R for a small amount of model verification, statistical analysis and chart drawing. When writing algorithms and databases , network service interaction and other situations when moving to Python. In order to reduce learning costs.

In addition, Python itself is a universal language. In addition to data science, it can also be widely used in network development, website construction, game development, web crawlers and other fields. When you need to integrate system products and services, it can serve as A one-stop development language, and more importantly, Python can also be used as a glue language to easily integrate with languages ​​with better performance such as C/C. In short, Python is a programming language that is simple and easy to learn but powerful and worth investing in, so we will use Python for introduction here.

If you want to compare Python and R, here are two articles you can refer to: The Peak Showdown between R and Python, and Which is better for data analysis: R or Python?.

How to get started with machine learning?

In fact, data science is an interdisciplinary subject. In the process of learning how to use Python for machine learning, you usually must master the following knowledge:

Machine learning algorithm

Python programming language and data analysis function library

Linear algebra/statistics and other related subjects

Domain knowledge in professional fields

In order to master the above three Large domain knowledge (we will first focus on the core techniques of machine learning and temporarily ignore the mastery of domain knowledge in data science). Specifically, we can refer to the following steps:

1. Master Basic Python programming language knowledge

Online learning resources:

o Codecademy

o DataCamp (you can also learn R)

o Learn X in Y Minutes(X = Python)

o Learn Python theHard Way

2. Learn basic math/statistics and machine learning basics

o Khan Academy Linear Algebra

o Introto Deive Statistics

o Introto Inferential Statistics

o Andrew Ng Machine Learning Course

o Andrew Ng Machine Learning Notes

o CarnegieMellon University Machine Learning

o Machine Learning Foundations

3. Know how to use Python scientific computing libraries and packages

It is recommended to install Anaconda, which supports multiple versions of Python across platforms. By default, data analysis and scientific computing suites are installed. It comes with spyder editor and JupyterNotebook (IPythonNotebook), which can provide a web version interface so that users can Develop and maintain Julia, Python or R programs through the browser.

o numpy: scientific analysis, ScipyLecture Notes teaching files

o pandas: data analysis

o matplotlib: good at drawing

o scikit-learn: Machine Learning Tools

4. Use scikit-learn to learn Python Machine Learning Applications

o MachineLearning: Python Machine Learning: Using Python

5. Use Python to implement machine learning algorithms

o Perceptron

o Decision tree

o Linear regression

o k-means clustering

6. Implement advanced machine learning algorithms

o SVM

o KNN

o RandomForests

o Reduce dimensionality

o Verify model

7. Understand the implementation and application of deep learning (DeepLearning) in Python

o NTU Applied DeepLearning

o Stanford DeepLearning

o Deep Learning (Deep Learning) Self-Study Material Recommendation

o Deep Learning Deep Learning: Collection of Chinese Learning Resources

If you want to know more about related learning, please pay attention to the php training column!

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