Machine learning methods are used for data analysis, this is where they are similar to data mining, but the main goal of machine learning is to automate decision models. Algorithms are the heart and soul of machine learning and they help computers to find hidden insights.
So in essence machine learning algorithms need to learn. The machine needs to learn from data. Data will have multi dimensions- Type (quantitative or qualitative), amount (big or small size) and number of variables available to solve a problem. Learning algorithms should also be as general purpose as possible. We should be looking for algorithms that can be easily applied to a broad class of learning problems.
The data scientists are responsible for machine learning and getting outputs but the business people are the ones who are going to use it for business purpose so the rules and insights extracted from machine learning should be interpretable. So the output produced by the machine has to be understood by humans, who may not be from the machine learning area.
The training aims at providing the participants with latest and general purpose machine learning algorithms. At the same time the training aims to deliver some common threads or a common knowledge base which can be used in future for learning a wide range of algorithms.