# Learning Units

# 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!

**7 chapters**

#### K-Nearest Neighbours

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.

**3 chapters**

#### Linear Regression

Linear regression is a very important regression technique. It is used in many situations and is also a key step towards understanding neural networks.

**7 chapters**

#### 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.

**6 chapters**

#### Model Selection

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.

**7 chapters**

#### K-Means

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.

**6 chapters**

#### Logistic Regression

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.

**4 chapters**

#### Mean Shift

Mean shift is a common clustering technique. It is a non-parametric technique that is commonly used in computer vision and image processing.

**4 chapters**

# Coming Soon…

#### Decision Trees

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.

**9 chapters**

#### DBSCAN

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.

**4 chapters**

#### Gaussian Processes

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.

**5 chapters**

#### 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.

**5 chapters**