Use this Python Cheatsheet to learn clever machine learning tricks for predictive analysis, Scikit-Learn, Jupyter notebooks, data visualization, and Pandas.
Machine Learning Cheat Sheet Support Vector Machines c s Creativity skills Decision boundary Support Vector Machine Classification Support vectors c s Creativity skills Computer Scientist Artist Decision boundaries Machine Learning Classification Main idea: Maximize width of separator zone →increases „margin of safety“ for classification. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these. Python 2.7 Quick Reference Sheet. Cheat sheets for machine learning are plentiful. Quality, concise technical cheat sheets, on the other hand. A good set of resources covering theoretical machine learning concepts would be invaluable. Shervine Amidi, graduate student at Stanford, and Afshine Amidi, of MIT and Uber, have created just such a set of resources. Python Pandas Cheat Sheet for Data Science & Machine Learning Category Python Cheat Sheet Posted on July 7, 2020 July 7, 2020 Author Haresh Makwana Leave a comment This Pandas cheat sheet through the basics of Pandas that you will need to get started on wrangling your data with Python. First of all, machine learning is a highly iterative field. This would entail a loop cycle of the above steps, where each cycle is based on the feedback from the previous cycle, with the goal of improving the model performance.
As the role of machine learning increases in importance so has the use of Python. Although the Python syntax is easy, if you’re one of the many engineers using Python to build your algorithms, you are always running on a tight project deadline. That’s where ActivePython and valuable tips and tricks like these come handy. Need to unstack a table? Or tired of rendering static plots in Jupyter using MatPlotlib? This Python Cheatsheet offers some neat tricks that can help you jump steps and save time when working on machine learning projects.
Python-cheatsheet-MLWhile these unique tips for Python and machine learning are great to keep handy, one of the time consuming tasks that data scientists and ML engineers face is resolving dependencies. That’s where ActivePython comes in.
We’ve built the hard-to-build packages so you don’t have to waste time on configuration…get started right away!
ActivePython comes bundled with the most popular machine learning Python packages. Precompiling these packages means you and your team save time on package management–allowing more time for writing valuable algorithms and models. No need for additional compiler configuration, settings and builds–just install ActivePython and you’re ready to go.
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Additionally, ActivePython is also a trusted Python distribution used by modern enterprises all over the world.
While the open source distribution of Python may be satisfactory for an individual, it doesn’t always meet the support, security, or platform requirements of large organizations. This is why organizations choose ActivePython for their data science, big data processing and statistical analysis needs.
Download ActivePython Community Edition to get started or contact us to learn more about using ActivePython in your organization.
You can also start by trying our mini ML runtime for Linux or Windows that includes most of the popular packages for Machine Learning and Data Science, pre-compiled and ready to for use in projects ranging from recommendation engines to dashboards.
Related Resources:
Top 10 Python Packages for Machine Learning
This post updates a previous very popular post 50+ Data Science, Machine Learning Cheat Sheets by Bhavya Geethika. If we missed some popular cheat sheets, add them in the comments below.
Cheatsheets on Python, R and Numpy, Scipy, Pandas
Data science is a multi-disciplinary field. Thus, there are thousands of packages and hundreds of programming functions out there in the data science world! An aspiring data enthusiast need not know all. A cheat sheet or reference card is a compilation of mostly used commands to help you learn that language’s syntax at a faster rate. Here are the most important ones that have been brainstormed and captured in a few compact pages.
Mastering Data science involves understanding of statistics, mathematics, programming knowledge especially in R, Python & SQL and then deploying a combination of all these to derive insights using the business understanding & a human instinct—that drives decisions.
Here are the cheat sheets by category:
Cheat sheets for Python:
Python is a popular choice for beginners, yet still powerful enough to back some of the world’s most popular products and applications. It's design makes the programming experience feel almost as natural as writing in English. Python basics or Python Debugger cheat sheets for beginners covers important syntax to get started. Community-provided libraries such as numpy, scipy, sci-kit and pandas are highly relied on and the NumPy/SciPy/Pandas Cheat Sheet provides a quick refresher to these.
- Python Cheat Sheet by DaveChild via cheatography.com
- Python Basics Reference sheet via cogsci.rpi.edu
- OverAPI.com Python cheatsheet
- Python 3 Cheat Sheet by Laurent Pointal
Cheat sheets for R:
The R's ecosystem has been expanding so much that a lot of referencing is needed. The R Reference Card covers most of the R world in few pages. The Rstudio has also published a series of cheat sheets to make it easier for the R community. The data visualization with ggplot2 seems to be a favorite as it helps when you are working on creating graphs of your results.
At cran.r-project.org:
At Rstudio.com:
- R markdown cheatsheet, part 2
Others:
- DataCamp’s Data Analysis the data.table way
Cheat sheets for MySQL & SQL:
For a data scientist basics of SQL are as important as any other language as well. Both PIG and Hive Query Language are closely associated with SQL- the original Structured Query Language. SQL cheatsheets provide a 5 minute quick guide to learning it and then you may explore Hive & MySQL!
- SQL for dummies cheat sheet
Cheat sheets for Spark, Scala, Java:
Apache Spark is an engine for large-scale data processing. For certain applications, such as iterative machine learning, Spark can be up to 100x faster than Hadoop (using MapReduce). The essentials of Apache Spark cheatsheet explains its place in the big data ecosystem, walks through setup and creation of a basic Spark application, and explains commonly used actions and operations.
- Dzone.com’s Apache Spark reference card
- DZone.com’s Scala reference card
- Openkd.info’s Scala on Spark cheat sheet
- Java cheat sheet at MIT.edu
- Cheat Sheets for Java at Princeton.edu
Cheat sheets for Hadoop & Hive:
Hadoop emerged as an untraditional tool to solve what was thought to be unsolvable by providing an open source software framework for the parallel processing of massive amounts of data. Explore the Hadoop cheatsheets to find out Useful commands when using Hadoop on the command line. A combination of SQL & Hive functions is another one to check out.
Cheat sheets for web application framework Django:
Django is a free and open source web application framework, written in Python. If you are new to Django, you can go over these cheatsheets and brainstorm quick concepts and dive in each one to a deeper level.
- Django cheat sheet part 1, part 2, part 3, part 4
Python Machine Learning Cheat Sheet Datacamp
Cheat sheets for Machine learning:
Python Machine Learning Cheat Sheet
We often find ourselves spending time thinking which algorithm is best? And then go back to our big books for reference! These cheat sheets gives an idea about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.
Scikit-learn Cheat Sheet Python Machine Learning
- Machine Learning cheat sheet at scikit-learn.org
- Scikit-Learn Cheat Sheet: Python Machine Learning from yhat (added by GP)
- Patterns for Predictive Learning cheat sheet at Dzone.com
- Equations and tricks Machine Learning cheat sheet at Github.com
- Supervised learning superstitions cheatsheet at Github.com
Cheat sheets for Matlab/Octave
Machine Learning Cheat Sheet Pdf
MATLAB (MATrix LABoratory) was developed by MathWorks in 1984. Matlab d has been the most popular language for numeric computation used in academia. It is suitable for tackling basically every possible science and engineering task with several highly optimized toolboxes. MATLAB is not an open-sourced tool however there is an alternative free GNU Octave re-implementation that follows the same syntactic rules so that most of coding is compatible to MATLAB.
Cheat sheets for Cross Reference between languages
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