If you want to brush up on your knowledge of machine learning, one of the best places to start is to understand the terminology and concepts involved. Neural networks, regression analysis and clustering are three of the most commonly used machine learning techniques used today. Neural networks, also referred to as deep learning, are algorithms that are modelled on the way human brains process information. Regression analysis is used to predict a continuous outcome variable based on one or multiple predictor variables. Finally, clustering is a tool that can identify patterns in data and group them accordingly.
One of the most important skills machine learning professionals interact with is data. They must have knowledge of software engineering and data science to be successful. Languages such as SQL, Python, C++, R and Java are used in stats analysis and data modelling. And of course, having a strong knowledge of mathematics and statistics is also necessary. Critical thinking and problem solving skills can be incredibly useful when explaing processes to colleagues with a non-technical background.
Learning resources are abundant and easy to find – many of them free – with courses such as Understanding Machine Learning available with no coding involved. Or if you’re looking for something more advanced, MIT’s course gives users an introduction to machine learning and how to apply the technology in a business context. Google’s Machine Learning Crash Course, hosted by Googlers, also offers 25 lessons on the subject.
Yahoo’s Zuoyun Jin has also provided some invaluable insights with respect to machine learning skills, based on his experience as a research engineer. This provides a useful reference point if one wishes to develop an area of expertise.
In conclusion, mastering machine learning takes time, patience and dedication if it is to be successful. However, with the right resources and knowledge available, it is an achievable task that will open up many doors, such as working in data science, AI and medtech.
