Machine Learning
Introduction
Machine Learning (ML) is a field devoted to understanding and building methods that let machines "learn" - that is, methods that leverage data to improve computer performance on set of tasks. -- Wikipedia - Machine Learning
Machine learning algorithms are an increasingly popular problem-solving tool. Rather than rely on direct programming instructions to solve a task, ML algorithms find patterns in data that correspond to a specific output. Computer science and mathematics study this branch of algorithms.
In practice, the data being sorted can be an entire data warehouse or dozes of data sensors relaying real-time information. The sheer amount of data processing required to produce an accurate ML model also requires tremendous computing power. This is why there has been a continual increase in interest in ML over this decade: as devices get more powerful, so do the ML algorithms that run on them.
In order for ML Algorithms to work, a set of input data called training data must be used. In basic terms, the algorithm will ingest the training data and compute a desired result based on the characteristics discovered in training data. There are two categories of ML data and each of the following techniques falls under on of the two.
The first ML category is supervised learning. In this type of algorithm, the input data has the answer key attached to the dataset. This means that the algorithm already knows all of the answers while it is being trained and can work from those answers to optimize the solution.
The second category of ML is unsupervised learning. In unsupervised learning, the answer key is not present in the training data. There are many different ways to implement this approach, most of which take time and experience. The unsupervised learning learning algorithms will memorize the outcome of events or data points and generate a desired output after learning how these outcomes were generated.
Reinforcement Learning
Reinforcement learning is a type of unsupervised learning algorithm. Reinforcement learning focuses on optimizing the path to a desired solution.
Neural Network
Neural Networks are a type of supervised learning algorithm that is used across the data engineering and science fields. Inspired by biological neurons and complex pathways between neurons in the brain, neural networks are incredibly receptive to errors in the training data. Typically, neural networks are made out of a dense web of interconnected neurons in which each neuron takes a set of values (real-world inputs) and outputs a decision.
K-Means Clustering
K-Means Clustering is a method of vector quantization,
originally from signal processing, that aims to partition
Confusion Matrix
A confusion matrix is a table or matrix that allows analysis of the accuracy of an ML algorithm in more nuanced ways than simply scoring overall accuracy as a single ratio. The table lays out on each axis all possible classifications of the data. Then usually the horizontal axis represents the predicted values and the vertical axis represents the actual values. Then each cell represents the frequency of the predicted values against their actual values as given by the data.