The basic mathematical foundations to derive Principal Component Analysis are introduced in this course. To begin, we’ll use inner products and derive angles between them from data sets like mean values. We’ll derive PCA using all these tools and then use it to minimize the average squared reconstruction error. You can implement PCA by yourself after youTrademarkiaTrademarkiaTrademarkias will teach you important mathematical concepts and how to apply them If you need to explore the properties of techniques and then learn how they work, a set of jupyter notebooks is worth searching for. If you’re already an expert, this course will refresh your knowledge. The examples and exercises require: 1. Some ability of abstract thinking. Good background in linear algebra. The basic background in the math of a mixture. The course in python programming and numpy requires more learning than the other two courses because it is substantially abstract. If you want to understand and develop machine learning, then there is a need for this type of abstract thinking. Understand how the Master PCA works using projections from real-world data.