What is machine learning?

Machine learning is crucial to bridging the gap between user intention and technological output. The key to machine learning is its ability to improve over time as more data becomes available and is provided to the system’s algorithm to analyze.

A machine learning algorithm basically consists of three parts:

• a decision process, which makes predictions and classifications based on input data;
• a loss function, which evaluates a prediction of the model and compares it against other known examples to measure performance;
• and a model optimization process, which applies and adjusts the model’s parameters to reduce discrepancies between the example and the model’s prediction. 

Machine learning algorithms have many applications in modern technology. Some common examples include powering speech recognition technology, acting as the AI in recommendation engines, facilitating automated stock trading and more.

The types of machine learning refer to the amount of human intervention required to ensure the model’s accuracy over time.

• Supervised learning is when the machine is taught by example. Operators provide an algorithm with a known data set that includes the inputs and outputs required. The operator knows the answers to the problem and the algorithm identifies patterns and makes predictions. The operator then corrects the algorithm and repeats the cycle.
• Semi-supervised learning is the same as supervised learning but uses both labeled and unlabeled data so the algorithm can become familiar with more complex decisions.
• Unsupervised learning involves the algorithm studying data to identify patterns without a clear answer provided, allowing it to determine correlations independently with only the data it has available, as well as historical knowledge, and organize it in structures. Unsupervised learning has three primary uses, training natural language processing models, clustering similar data for segmentation and reducing the number of variables needed to find the correct information, known as dimension reduction.
• Reinforcement learning is when a machine is provided with a set of actions, parameters and end values to explore different options to determine the optimal solution. It is essentially trial and error on a much larger scale, performed in an efficient manner.