Introduction:

• Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

• Machine learning is an important component of the growing field of data science.

• With the use of statistical methods, algorithms are trained to make classifications or predictions and to uncover key insights in data mining projects.

• These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase.

• They will be required to help identify the most relevant business questions and the data to answer them.

How machine learning works ?

The learning system of a machine learning algorithm into three parts.

1. A Decision Process

• machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate of a pattern in the data.

2. An Error Function

• An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.

3. A Model Optimization Process

• If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate.

Machine Learning Methods

• There are fall into three categories.

  1. Supervised Machine Learning
  2. Unsupervised Machine Learning
  3. Reinforcement Machine Learning

• Supervised Machine Learning

  • Supervised learning is the type of machine learning in which machines are trained using well “labeled” training data, and on basis of that data, machines predict the output.

  

• Unsupervised Machine Learning

  • Unsupervised learning is a type of machine learning in which models are trained using unlabeled datasets and are allowed to act on that data without any supervision.

  

• Reinforcement Machine Learning

  • Reinforcement machine learning is a machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data.

  • This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.