Machine Learning (abbreviated as “ML”) Algorithms are programs (math and logic) that alter themselves to perform better as they are exposed to more information. The “learning” portion of ML implies that those projects change how they process information over time, much as humans change how they process data by learning. So a ML algorithm is a program with a particular way to adjusting its own parameters, given feedback on its past performance making forecasts/predictions about a dataset.
1. Supervised Learning: It is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
2. Un-supervised Learning: The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. Algorithms are left to their own devises to discover and present the interesting structure in the data.
3. Reinforcement Learning Algorithms: Reinforcement learning is a type of machine learning in which a computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment. This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task. Some sub-types are:
4. Natural Language Processing: NLP is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyse large amounts of natural language data.
5. Dimensionality Reduction Algorithms Dimensionality reduction is the process of reducing the dimension of your feature set. It is bringing the number of columns down to say, twenty or converting the sphere to a circle in the two-dimensional space. Some sub-types are: