Setting up your Python environment for ML

Transcript

1. Setting up your Python Environment for ML Name : Prajjwal Nijhara Enrol. No : GK 9192 Fac. No. : 18EEB237
2. More about me Twitter: @NijharaPrajjwal GitHub: @pnijhara Blogs: https://pnijhara.github.io
3. Why Python for ML?
4. Simplicity
5. Semantics Not only has the syntax been praised for its simplicity but also for being described as “math-like.” It has been noted that the semantic presentation is similar to many mathematical concepts, which lends itself well to the math necessary for ML.
6. - Free and Open Source - Popularity - Easy to use Libraries - Custom APIs
7. Why not to use Python - Heavy in nature - Not compiled (Slow) - Heavy libraries such as Pandas, Numpy etc - Rule base hell
8. Alternative language JULIA
9. How to setup Python environment First of all to perform machine learning and deep learning on any dataset, the software/program requires a computer system powerful enough to handle the computing power necessary. - CPU - RAM - GPU - OS
10. The hardest way is the best way - PIP - sudo apt-get install python3-pip - Virtual env - sudo pip install virtualenv virtualenvwrapper - mkvirtualenv ml - workon ml
11. The easy way is the pathetic way - Conda - Jupyter
12. Conclusion
13. References - https://www.jigsawacademy.com/blogs/data-science/julia-vs-python/ - https://medium.com/@devathon_/julia-vs-python-in-2020-d2dc2c2ef3f - https://developer.ibm.com/blogs/why-machine-learning-is-primarily-written-in- python/ - https://towardsdatascience.com/how-to-setup-a-python-environment-for-mach ine-learning-354d6c29a264 - https://towardsdatascience.com/setup-an-environment-for-machine-learning-a nd-deep-learning-with-anaconda-in-windows-5d7134a3db10 - https://pnijhara.github.io/2021/05/install-chocolatey-on-windows/ -