My Top 10 Favourite Machine Learning Algorithms

When entering the machine learning world, there is a LOT of information. There are lots of different courses, techniques, terms, and algorithms. How would one know what to focus on when learning?

That’s what I want to help you with today! So you do not have to go down too many rabbit holes, I present my favourite machine learning algorithms!

1. Linear Regression

This algorithm is so common that I have yet to have a job where this tool was not useful! With linear regression, we can do all kinds of things. I have used it to help my friends value cars. I have used it to forecast revenues, estimate marketing effectiveness, and much more!

Linear regression is the backbone of finance, business and science. This should definitely be one of the first algorithms one should master before getting into other ones.

2. Logistic Regression

This model is handy for predicting binary (e.g., yes/no, positive/negative) variables such as sentiment analysis in reviews or the probability a customer will return.

3. K-Means

This tool is so useful on a day-to-day basis. I have used it for grouping customer feedback to work out common ‘themes’ in the comments. I used it to find common job groupings when job hunting! This tool, along with linear regression should definitely be part of your machine learning Swiss army knife.

4. K Nearest Neighbours (KNN)

I have used KNN quite a few times, especially in the data preprocessing stage. It can be used to fill erroneous or missing data points. It is also often used as a baseline algorithm for recommendation systems.

5. Decision Trees

Decision trees are a must-have in your machine learning tool belt. There are not too many simple and easily-explainable models like this one.

6. Random Forests


As they say, “There is wisdom in the counsel of many.” This algorithm is quite a powerful regression and classification algorithm. If Linear and logistic regression are the baselines, this algorithm steps it up.

I have used random forests when pricing property, as it was the best predictor of all the regression models I tried. After learning linear and logistic regression, this algorithm is well worth learning about!

7. Boosting Algorithms

Though there are quite a few different implementations, I would recommend XGBoost, which is the most widely used model. Some others include AdaBoost, Gradient Boosting, and CatBoost.

8. Principal Component Analysis (PCA)

PCA, basically, computes principal components which are vectors that are linear combinations of the original variables. You can, then, take the first few principal components to explain a large part of the variation in the data. Below, I have plotted some generated data in 3D and then in 2D after keeping the first two principal components.

As you can see above, the data are reduced down to 2D whilst keeping most of the information. PCA is a critical tool to have in your toolbelt!

9. Neural Networks

Once you have a grasp of the more common algorithms, these babies are a must! They will meet the challenge in regression, classification, signal processing, and more!

10. Support-Vector Machines (SVMs)

The model is beautifully simple and effective if the data are linearly separable. Later, the algorithm was improved to include kernels that would transform the input data space. This allows for ‘non-linear’ separations between classes.


Each of these algorithms and models has its strengths and use cases, so it is important to gain an understanding of the ML tools!

At Queenstown Resort College (QRC), we are launching our new micro-credential in machine learning in beautiful New Zealand. Come join us and learn the skills you need for your next career steps!

Find more about our Machine Learning Fundamentals micro-credential here.

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