Machine Learning with Coffee

20 Perceptron: Machine Learning Begins

We introduce the concept of a perceptron as the basic component of a neural network. We talk about how important is to understand the concept of backpropagation applied to a single neuron.

19 ICA: Independent Component Analysis

We discuss Independent Component Analysis as one of the most popular and robust techniques to decompose mixed signals. ICA has important applications in audio processing, video, EEG and in many datasets, which present very high multicollinearity.

18 PCA: Principal Component Analysis

We discuss Principal Component Analysis as one of the most popular techniques to reduce the dimensionality of a dataset. PCA helps us be more efficient in terms of the number of variables we feed to our machine learning models.

17 Anomaly Detection: Clustering

We present 3 clustering algorithms which will help us detect anomalies: DBSCAN, Gaussian Mixture Models and K-means. These 3 algorithms are very popular and basic but have passed the test of time. All these algorithms have many variations which try to overcome some of the disadvantages of the original implementation.

16 Anomaly Detection: Control Charts

Anomaly detection is not something recent, techniques have been around for decades. Control charts are graphs with solid mathematical and statistical foundations which monitor how a process changes over time. They implement control limits which automatically flag anomalies in a process in real-time. Depending on the problem at hand, control charts might be a better alternative to more sophisticated machine learning approaches for anomaly detection.