Machine Learning Part 3! Unsupervised

This is the last part of my series blog. So sad. For the last three weeks, I have been explaining machine learning and the essence of it. Part one is found here and that is the intro, the second part can be found here. In part two I began explaining the machine learning used for supervised learning. For this final series, I will continue to explain and unsupervised learning.

I have begun explaining machine learning and compared it to a car and its engine. I began explaining supervised learning to automatic cars because of the need to not change the gears. I compared it to this because the supervised learning dataset it has the target variable therefore the model identifies patterns. No with unsupervised learning, I compare it with manual cars because of the need to switch gears. With manual cars, the need to change gears is because of the need to change the power when you are on flat road, uphill, or downhill.

Unsupervised Learning Models

Clustering

This technique of clustering is very interesting! It involves grouping or ‘clustering’ of the data points. It begins with a certain amount of centroids and then the points rearrange to fit closer to the centroids.

PCA

The next model, Principal Component Analysis is a non-parametrical technique. It is mainly used with higher dimensional to then lower it to at least 2 dimensions but keeping the original variables. A better description and explanation can be found here in this article.

Conclusion

This has been a fun time writing about machine learning. I hope it has been useful for anyone to comes across. I hope my comparison to cars might have made it a bit easier. Any questions and feedback are welcomed!