Machine learning is a wide-ranging collection of techniques and algorithms used for data analytics, in which a model is ‘trained on” (or “learns from”) existing data. Typically the ultimate goal is to use the model for predictive purpose on new data. There is no “one size fits all” solution to a given machine learning application.

Challenges for machine learning are data diversity, lack of domain tools, time consuming, over fitting, speed-accuracy-complexity problem. MATLAB is able to provide solutions for those challenges. MATLAB strengths for machine learning include the extensive data support, high-quality libraries, interactive and app-driven workflows, integrated best practices (model validation tools built into app, rich documentation with step-by-step guidance), and flexible architecture for customized workflow.

Machine learning is used when hand written rules and equations are too complex, when rules of a task are constantly changing and when the nature of the data changes and the program needs to adapt.

Machine learning is used in many applications such as image recognition, speech recognition, stock prediction, medical diagnosis, data analytic, robotics, etc.

If you need to implement algorithms that iteratively learn from data to find hidden insights without being explicitly programmed where to look and to automate analytical model building, “Machine Learning with MATLAB” training will equip you with knowledge to achieve your objectives.