Machine Learning for Everybody!

Who is machine learning for? Everybody!

Machine Learning for Everybody!
Screenshot from Machine Learning for Everybody


It's 2022. Who is machine learning for?

Machine learning is for everybody!

Or, at least, that's the name of a new video course from feeCodeCamp, put together by instructor Kylie Ying. The course aims to bring machine learning fundamentals to complete beginners.

From the course video YouTube page:


Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.


Not only does the course introduce concepts to newcomers, but it gets you up implementing these concepts with code straight away. Thus, while this is a course for beginners to machine learning, it's not for beginners to coding.

First off, the course starts by covering the absolute basics, such as introductory Google Colab, the basics of what machine learning is, what features are, what classification and regression are, and what it means to prepare data and to train a model. You then wade into the machine learning waters and cover the theory and implementation of algorithms such as K-nearest neighbors, naive Bayes, logistic regression, and support vector machines.

Attention then turns to neural networks and TensorFlow for the remainder of the course. You will learn to implement classifiers in TensorFlow, learn about linear regression with neurons, implement regression neural networks using TensorFlow, and k-means clustering. The course finishes off with principal component analysis and an implementation of k-means and PCA.

Find the course here, as well as below.



Kylie does a great job making sure that machine learning is for everybody. Your job is to have fun making it for you.

Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.