I offer fundamental and advanced machine learning ideas in this tutorial. I tried to cover all of the principles that I felt could be relevant to you all. When I was learning, I coded and documented this repository. Feel free to ask anything I'd be happy to help.
-
What is Machine Learning?
- Machine Learning is a system of computer algorithms that can learn from example by improving oneself, without being coded by a programmer explicitly. Machine learning is an artificial intelligence component that combines information with statistical methods to forecast the output to produce meaningful insights.
-
Need for Machine Learning
- The rationale for the necessity for machine learning is the ability to execute activities that are too complicated to be implemented directly by a person. As human beings we have some restrictions because we are unable to access the vast amount of data manually, thus we need certain computer systems and the machine learning comes here to make things simpler for us.
-
Classification of Machine Learning
-
Supervised learning : Supervised learning is when the model is getting trained on a labelled dataset. Labelled dataset is one which have both input and output parameters.
-
Unsupervised learning: It is a sort of learning, where our model is not aimed, i.e. training model just has input parameter values. The model must find out how it can learn by itself.
-
Semi-supervised Learning: As the name suggests, its working lies between Supervised and Unsupervised techniques.
-
Reinforcement learning: In this technique, model keeps on increasing its performance using a Reward Feedback to learn the behavior or pattern. These algorithms are specific to a particular problem.
-
