A quick start guide and best practices to working with Docker. As developers, data scientists, software engineers, we work on complex code bases that depend on many items in the background.
We have all been there,
“_It worked on my machine!!_”.
Who wasn’t on either end of that statement?
As developers, data scientists, software engineers, we work on complex code bases that depend on _many _items in the background. When we want to share our code with colleagues or put up on Github as an opensource project, we need to _ensure _that the code will work on all different environments.
Sometimes — more often than we would like to admit — we try to run a friend’s code or a code that we got from the internet when the computer yells at us “Import Error.” That error means that the code needs more information that it can’t find on your computer.
The solution for this is using Docker. *Docker *is a container management system that aims to facilitate sharing projects and to run them across different environments. Basically, Docker makes it easy to write and run codes smoothly on other machines with different operating systems by encapsulating the code and all its dependencies in a container.
This container makes the code self-contained and independent from the operating system.
Image by the author (made using Canva)
When we write code for data science or machine learning applications, we often have many concerns that make using a Docker the best option for our applications. These concerns are:
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
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Applied Data Analysis in Python Machine learning and Data science, we will investigate the use of scikit-learn for machine learning to discover things about whatever data may come across your desk.