# Detail list of learning materials of machine learning, Python and math learning

When learning this technology, it is best to have some quick reference manuals on hand, they list the key points that need to be understood. Robbie Allen has compiled more than 20 quick reference materials related to machine learning, and shared them, and may also help others who learn this technology.

The field of machine learning is undergoing rapid changes, and these materials will one day be out of date, but at least for the time being, they are still very useful. If you don’t want to download these materials one by one, you can download all the materials from here.

Below are some diagrams of machine learning algorithms, very useful.

- Neural network architecture: http://www.asimovinstitute.org/neural-network-zoo/
- Neural network architecture: http://www.asimovinstitute.org/neural-network-zoo/
- Microsoft Azure algorithm chart: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
- Microsoft Azure algorithm chart: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet

**SAS algorithm chart:**

- http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
- Algorithm summary: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
- http://thinkbigdata.in/best-known-machine-learning-algorithms-infographic/
- Comparison of the advantages and disadvantages of algorithms: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend
- http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
- Algorithm summary: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
- http://thinkbigdata.in/best-known-machine-learning-algorithms-infographic/
- Comparison of the advantages and disadvantages of algorithms: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend

**Python**

There are many Python-related learning courses on the Internet, and some of the best materials are listed below.

- Algorithm: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
- Algorithm: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
- Python basics: http://datasciencefree.com/python.pdf
- https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
- Python basics: http://datasciencefree.com/python.pdf
- https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
- Numpy: https://www.dataquest.io/blog/numpy-cheat-sheet/
- http://datasciencefree.com/numpy.pdf
- https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
- Pandas: http://datasciencefree.com/pandas.pdf
- https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
- Matplotlib: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb
- Scikit Learn: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk
- http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
- https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
- TensorFlow: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb
- PyTorch: https://github.com/bfortuner/pytorch-cheatsheet
- Numpy: https://www.dataquest.io/blog/numpy-cheat-sheet/
- http://datasciencefree.com/numpy.pdf
- https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb
- Pandas: http://datasciencefree.com/pandas.pdf
- https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb
- Matplotlib: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
- https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb
- Scikit Learn: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk
- http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
- https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb
- TensorFlow: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb
- PyTorch: https://github.com/bfortuner/pytorch-cheatsheet

**Mathematics**

If you want to learn machine learning, you need to understand statistics, linear algebra, and calculus. The following information can help you understand the mathematics behind machine learning.

- Probability: http://www.wzchen.com/s/probability_cheatsheet.pdf
- Linear Algebra: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
- Statistics: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
- Calculus: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N
- Probability: http://www.wzchen.com/s/probability_cheatsheet.pdf
- Linear Algebra: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
- Statistics: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
- Calculus: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N