Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. This course introduces you to one of the prominent modelling families of Unsupervised Machine Learning called Association Rule Learning. Association rule mining helps find exciting connections and linkages among large data items. The association rule learning is employed in Market Basket analysis, Web usage mining, Continuous production, Customer analytics, Catalogue design, Shop layout, Recommender systems etc. Association rules are critical in data mining for analyzing and forecasting consumer behaviour.

These 101 algorithms are equipped with cheat sheets, tutorials, and explanations. Think of this as the one-stop shop/dictionary/directory for machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms with useful Python tutorials, R tutorials, and cheat sheets from Microsoft Azure ML, SAS, and Scikit-Learn to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone.

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent. A typical and widely used example of association rules application is market basket analysis.

In the first part of our review of AI technologies and current trends, we already talked about RPA, computer vision, and chatbots. In this article, we will talk about data collection and processing of the received information to improve the effectiveness of marketing, etc. Both marketers and business owners know John Vanamemaker's phrase: "I know that half of my advertising budget is wasted, but I don't know which one." With new AI-based technologies for data collecting and analyzing it has finally lost its relevance. The marketing department will no longer have a hard time putting together the entire sparse array of customer information for its reports of the advertising campaigns effectiveness: why people leave any page of the site, why they refuse to buy a product or service.

Data Mining enables users to analyse, classify and discover correlations among data. One of the crucial tasks of this process is Association Rule Learning. An important part of data mining is anomaly detection, which is a procedure of search for items or events that do not correspond to a familiar pattern. These familiar patterns are termed anomalies and interpret critical and actionable data in various application fields. This concept can be best understood with the supermarket example.