Machine Learning  is a branch of artificial intelligence that develops algorithms by learning the hidden patterns of the datasets used it to make predictions on new similar type data, without being explicitly programmed for each task.

The capability of a machine to imitate intelligent human behaviour.

Machine Learning allows the systems to make decisions autonomously without any external support. These decisions are made when the machine is able to learn from the data and understand the underlying patterns that are contained within it.

Types of Machine Learning

There are several types of machine learning, each with special characteristics and applications. Some of the main types of machine learning algorithms are as follows:

Supervised Machine Learning

Unsupervised Machine Learning

Semi-Supervised Machine Learning

Reinforcement Learning

Machine Learning Life Cycle

The machine learning lifecycle is a process that guides the development and deployment of machine learning models in a structured way. It consists of various steps.
Each step plays a crucial role in ensuring the success and effectiveness of the machine learning solution. 

The steps to be followed in the machine learning lifecycle are:

Problem Definition

Data Collection

Data Cleaning and Pre-processing

Exploratory Data Analysis (EDA)

Feature Engineering and Selection

Model Selection

Model Training

Model Evaluation and Tuning

Model Deployment

Model Monitoring and Maintenance