Introduction to Supervised Learning


In computer science, machine learning refers to a type of data analysis that uses algorithms that learn from data. It is a type of artificial intelligence (AI) that provides systems with the ability to learn without being explicitly programmed. This enables computers to find data within data without human intervention. 
Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: 1) supervised 2) unsupervised learning

Supervised learning systems are mostly associated with retrieval-based AI but they may also be capable of using a generative learning model.

With supervised learning, data is separated into three groups: train, dev and test datasets. The training dataset is used to train the model. The dev dataset is used to test the model during the model development, but not during its training. The test dataset is used when the model is complete to see how it reacts to data it has never seen before. We have to choose relevant fields in a dataset. Sometimes information just isn’t relevant and should not be included in a dataset.

Generally, supervised learning operates with three main tasks: binary classification, multiclass classification and regression and we usually face three problems while working on supervised learning, they are collecting data, labeling data and prediction accuracy.

Applications of Supervised Learning:

1) Advertising/Marketing

  a) Lifetime value of customer

  b) Churn

  c) Sentiment Analysis

  d) Recommendations

2) Big data/Data Analytics

  a) Retention

  b) Human resource allocation

  c) Sales performance

3) Sales Applications

  a) Time series forecasting

4) Security

  a) Spam filtering

  b) Malicious links

  c) Fraud detection


6)Pattern recognition

7)Handwriting recognition

8)Speech recognition