What is Machine Learning?

• Machine learning (ML) is a field of study in AI - artificial intelligence.

• ML is focused on the development and study of statistical algorithms that can learn from data and make inferences about unseen data.

• AI is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment.

• AIs use learning and intelligence to take actions that maximize their chances of achieving defined goals.

Applications of Machine Learning

Machine Learning is applied in the following fields:

• Natural language processing or NLP

• Computer vision

• Speech recognition

• Email filtering

• Agriculture

• Auto industry

• Finance

• Medicine

• Etc.
  

Types of Machine Learning

• Supervised Learning - algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data is labeled.

• Unsupervised Learning - algorithms find structures in data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. The UL algorithms uncover hidden trends in the data.

• Reinforced Learning - an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms.

How to Develop Machine Learning Models Using SEMMA

Sample: Identify different databases; select the data sample that's sufficient for the modeling process.

Explore: Understand the data, discover the relationships among variables, visualize the data, and get initial interpretations.

Modify: Prepare the data for modeling. This phase involves dealing with missing values, detecting outliers, transforming features, and creating new additional features.

Model: Select and applying different modeling techniques, such as linear and logistic regression, backpropagation networks, KNN, support vector machines, decision trees, and Random Forest.

Assess: In this last phase, the predictive models that have been developed are evaluated using performance evaluation measures.