Machine Learning is an application of AI that provides system the ability to automatically learn and improve without being explicitly programmed. And AI aims at making machine smarter by giving those capabilities to think on their own feet to make decisions or mimic human activities and one can think of ML as a subset of AI. It is categorised into: Supervised Learning , Unsupervised Learning and Reinforcement Learning .
Machine Learning focuses on the development of computer programs that can access data and use it to learn themselves. The learning process begins with observations of data, experience or instructions, this process helps to look for a pattern in data and make better decisions in the future.
SUPERVISED LEARNING:Training dataset contains input information and the worth we need to foresee.
The Supervised Learning model uses the training data to establish a link between the input and the output. Training data can be generalized and that the model can be used on new data with some accuracy.
Algorithms under Supervised Learning are Naive Bayes, Gradient Boosting, Neural Networks.
It is often used for image recognition, speech recognition and sometimes in financial analysis.
UNSUPERVISED LEARNING: Unsupervised Learning does not use output data, and can be split into different categories.
REINFORCEMENT LEARNING: These algorithms can be seen as the best possible way for earning the greatest reward. Rewards can be anything from earning money, winning a game or beating opponents.