ML: A Starter Guide to Machine Learning in Python for 2024

Learn the fundamentals of Machine Learning with Python using Sci-kit Learn. Includes code examples and model evaluation techniques.

Machine Learning (ML) is a game-changer in today’s world, allowing computers to learn from data and make decisions without needing specific instructions.

In this article, I’m going to break down ML in simple terms, focusing on how to use Python's Sci-kit Learn library. The goal is to help you really understand these ideas and show you how to apply them in your own projects.

We'll walk through the basics of ML, look at different types like supervised and unsupervised learning, explain why data preprocessing is important, and cover how to check if your model is working well.

Each week, I dive deep into Python and beyond, breaking it down into bite-sized pieces. While everyone else gets just a taste, my premium readers get the whole feast! Don't miss out on the full experience – join us today!

Plus, I’ll include easy-to-follow code examples using Sci-kit Learn so you can try it out yourself. This topic is far too big to cover in a single piece, therefore I’ll be branching out into a Machine Learning dedicated series at some point in the future.

This is all apart of my current series on Data Analytics where I started with an introduction into Data Analytics and have worked through stages of Exploratory Data Analytics into Data Visualization. This series is written for you guys, my readers!

Today I will cover the different types of learning, how you can get started building your own Models with Scikit-Learn and cover the different types of Models with a stronger focus on regression, but will also touch on K-Means clustering too!

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As a bonus I wrote a little about Scikit-Learn in one of my 3 Randoms articles. This showcased so ways you can use this machine learning library. I will link that here as it pairs well with this longer form article.

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Grab some coffee or tea, and get ready for some Machine Learning!