This paper collects and arranges the machine learning checklist, including**Machine learning, python, numpy, pandas, Matplotlib, linear algebra, calculus, statistics, probability theory**And other related quick reference tables. I have established an open source projectMachineLearningCheatsheets, I will continue to update various cheatsheets related to machine learning. Welcome to star.

## catalogue

# machine learning

## Neural network architecture

Neural network structure quick look-up table, including perceptron, feedforward neural network, radial basis function network, cyclic neural network, etc.

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## Azure machine learning algorithm

Microsoft azure machine learning has a large algorithm library from classification, recommendation system, clustering, exception detection, regression and text analysis series. Each aims to solve different types of machine learning problems.

Each machine learning algorithm has its own style or inductive bias. For a particular problem, several algorithms may be appropriate, and one algorithm may be more appropriate than others. However, it is not always possible to know in advance which method is most appropriate. In this case, several algorithms will be listed in the quick look-up table at the same time. The appropriate strategy is to try one algorithm. If the results are still unsatisfactory, try other algorithms.

## SAS machine learning algorithm

This resource is mainly designed for beginners to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to solve problems they are interested in.

Even experienced data scientists cannot determine which algorithm performs best before trying different algorithms. We do not advocate a completion method, but we do want to provide guidance on which algorithms to try first based on some clear factors.

## The world of machine learning algorithms

This is a very good information diagram, which shows the basic types of machine learning algorithms.

## Data iku machine learning algorithm

We (data iku) briefly summarize the top-level algorithms used in prediction analysis, as you can see below.

# Python

## Python quick look-up table

## Python import data

## Numpy

Numpy supports a large number of dimensional array and matrix operations. It is a python library for array operations. I used to be inAdvanced machine learning seriesIt was said in the bookPre machine learning (III): master common numpy usage in 30 minutes。

## Pandas

Pandas provides fast, flexible and expressive data structures and is a powerful Python library for data analysis.

I have written nearly ten thousand words about pandas.

I used to be inAdvanced machine learning seriesIt was said in the bookPre machine learning (4): master the usage of pandas in one article。

Space relationship, the above is only part`pandas`

Please refer to the open source project for the quick look-up table picture and PDF original fileMachineLearningCheatsheets

## Matplotlib

Articles on Matplotlib are included inAdvanced machine learning series。

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## Scikit-Learn

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## spaCy

## Bokeh

# mathematics

## probability theory

Space relationship, the above is only part`probability theory`

Please refer to the open source project for the quick look-up table picture and PDF original fileMachineLearningCheatsheets

## linear algebra

Space relationship, the above is only part`linear algebra`

Please refer to the open source project for the quick look-up table picture and PDF original fileMachineLearningCheatsheets

## statistics

Space relationship, the above is only part`statistics`

Please refer to the open source project for the quick look-up table picture and PDF original fileMachineLearningCheatsheets

## Calculus

Space relationship, the above is only part`Calculus`

Please refer to the open source project for the quick look-up table picture and PDF original fileMachineLearningCheatsheets