Introduction to Machine Learning
Learn what Machine Learning is, how it differs from traditional computer science. Learn about regression, classification, clustering, and other Machine Learning tasks!
K-Nearest Neighbours is one of the oldest machine learning algorithms. It can be used for both classification and regression, and can also be used as a meta-learner.
Linear regression is a very important regression technique. It is used in many situations and is also a key step towards understanding neural networks.
Naive Bayes Classifiers
Naive Bayes Classifiers are a family of classifiers which rely on Bayes Theorem. They are a good example of generative models which can be used with many probability distributions.
Machine Learning is all about learning to become better at a certain task. Being able to evaluate how well we are doing at such a task is a core aspect that one must learn.
K-Means is one of the most popular clustering algorithms. It is easy to understand and also has some interesting variants in K-Medians and K-Medoids.
Logistic regression is a variant of linear regression used for classification tasks. Similarly, it is used in many situations and is also a key step towards understanding neural networks.
Mean shift is a common clustering technique. It is a non-parametric technique that is commonly used in computer vision and image processing.
Decision Trees are some of the most intuitive models out there. They are often used because they are most of the time easily interpretable by humans.
DBSCAN is short for Density-based spatial clustering of applications with noise. It is a useful clustering technique when dealing with noisy data of arbitrary shape.
Gaussian Processes are a powerful technique for regression. On top of being able to model high-dimensional function, it can provide a probability for each prediction.
Support Vector Machines
Support Vector Machines are powerful classifiers that can deal with high-dimensional and possibly non-linear data using what is called the “kernel” trick.