Python Cheatsheet: Tips and Tricks for Machine Learning
Use this Python Cheatsheet to learn clever machine learning tricks for predictive analysis, Scikit-Learn, Jupyter notebooks, data visualization, and Pandas.
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-ML
While 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.
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.
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.