Cheat Sheets #1: Machine learning, Python, Visualization, Data Science Libraries, Jupyter Notebook, Big-O & Math

6 min readMay 30, 2020

Learning Machine learning and Deep learning is difficult for newbies. As well as deep learning libraries are difficult to understand. I am creating this series with cheat sheets which I collected from different sources.

Over the past few months, totally redesigned the cheat sheets. The goal was to make them easy to read and beautiful so you will want to look at them, print them and share them.

Do read this and contribute cheat sheets if you have any. If you like this post, give it a ❤️! Here we go:

Cheat Sheets for Artificial Intelligence, Neural Networks, Machine Learning, Deep Learning & Data Science

1. Machine Learning Overview

2. Algorithm Pro/Con

Source: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend

3. Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

4. Machine Learning: Scikit-learn algorithm

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

`sklearn` **metrics** for classification and regression

5. [Microsoft Azure] MACHINE LEARNING : ALGORITHM CHEAT SHEET

This machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job.

6. Python Basics

Source: http://datasciencefree.com/python.pdf

Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA

7. Python for Data Science

8. Numpy

NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy.

9. Pandas

The name ‘Pandas’ is derived from the term “panel data”, an econometrics term for multidimensional structured data sets.

10. Data Wrangling

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

11. Data Wrangling with dplyr and tidyr

Data Wrangling with dplyr and tidyr Cheat Sheet

12. Scipy

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

13. Data Visualization

Matplotlib

matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[2] SciPy makes use of matplotlib.

pyplot is a matplotlib module which provides a MATLAB-like interface.[6] matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

14. PySpark

15. Big-O

BIG-O Algorithm Data Structure Operations

16. Math

If you really want to understand Machine Learning, you need a solid understanding of Statistics (especially Probability), Linear Algebra, and some Calculus. I minored in Math during undergrad, but I definitely needed a refresher. These cheat sheets provide most of what you need to understand the Math behind the most common Machine Learning algorithms.