Describing and picturing MLAlgos and Machine Learning is the main idea of this post. I will attempt to answer few basic questions as well. Though these questions have been answered many a times in the past and are widely available. Answering them again here from my very own experience on the ground may makes the difference though rather then simply answering from phd or scholar books material prospective.
The learning process for human child or new machine algorithm is same regardless of the fact that something is made up of bones and flash or wires and metal.
Machine learning is subset to Artificial Intelligence which borrows principles from computer science. It is not an AI though; It is focal point where business and experience meet emerging technology and decides to work together. ML also has very close relationship to statistics; which is a graphical branch of mathematics. It instructs an algorithm to learn for itself by analyzing data. The more data it processes, the smarter the algorithm gets. Until only recently even though foundation was laid down in 1950 ML remained largely confined to academia.
ML has Organizations have had success with each type of learning, but making the right choice for your business problem requires an understanding of which conditions are best suited for each approach. Types of machine learning algorithms i.e. MLAlgos and which one to be used when is extremely important to know. The goal of the task and all the things that are being done in the field and put you in a better position to break down a real problem and design a machine learning system.
Before we get into MLAlgos lets understand some basics here. The approach of developing ML includes learning from data inputs based on “What has happened”. Evaluating and optimising different model results remains focus here. As on date Machine Learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra.
Machine Learning is classified into four categories at high level depending on the nature of the learning and learning system. Some how I find difficult to accept Semi-supervised Learning.
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