With this free course of 57 video lessons you will learn about the most important concepts of Machine Learning
Machine learning is the subfield of computer science and a branch of artificial intelligence, which aims to develop techniques that allow computers to learn. An agent is said to learn when his performance improves with experience; that is, when the skill was not present in their genotype or birth traits.1 More specifically, machine learning researchers look for algorithms and heuristics to convert data samples into computer programs, without having to write the latter explicitly. The resulting models or programs must be able to generalize behaviors and inferences to a broader (potentially infinite) set of data.
In many cases, the field of action of machine learning overlaps with that of inferential statistics, since the two disciplines are based on data analysis. However, machine learning incorporates the concerns of the computational complexity of problems2. Many problems are NP-hard, so much of the research done in machine learning is focused on designing feasible solutions to those problems. Machine learning is also closely related to pattern recognition. Machine learning can be seen as an attempt to automate some parts of the scientific method using mathematical methods. Therefore it is a process of knowledge induction.
Machine learning has a wide range of applications, including search engines, medical diagnostics, credit card fraud detection, stock market analysis, DNA sequence classification, speech and written language recognition, gaming, and robotics.