Your First Machine Learning Project in Python Step-By-Step - Start Machine Learning in Python?
Python Can Be Intimidating When Getting Started
Python is a popular and powerful interpreted language. Unlike R, Python is a complete language and platform that you can use for both research and development and developing production systems.
There are also a lot of modules and libraries to choose from, providing multiple ways to do each task. It can feel overwhelming.
The best way to get started using Python for machine learning is to complete a project.
It will force you to install and start the Python interpreter (at the very least).
It will given you a bird’s eye view of how to step through a small project.
It will give you confidence, maybe to go on to your own small projects.
Beginners Need A Small End-to-End Project
Books and courses are frustrating. They give you lots of recipes and snippets, but you never get to see how they all fit together.
When you are applying machine learning to your own datasets, you are working on a project.
A machine learning project may not be linear, but it has a number of well known steps:
Define Problem.
Prepare Data.
Evaluate Algorithms.
Improve Results.
Present Results.
The best way to really come to terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps. Namely, from loading data, summarizing data, evaluating algorithms and making some predictions.
If you can do that, you have a template that you can use on dataset after dataset. You can fill in the gaps such as further data preparation and improving result tasks later, once you have more confidence.
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