Data has replaced oil as the new commodity. This claim demonstrates how data collection, storage, and analysis are the driving forces behind all contemporary IT systems. Whether it's about making business decisions, predicting the weather, researching the architectures of proteins in biology, or creating a marketing strategy. In each of these cases, the data analysis is based on a multidisciplinary approach that includes mathematical models, statistics, graphs, databases, and, of course, the commercial or scientific reasoning. Therefore, we want a programming language that can accommodate all of the many demands of data science. Python stands out as one such language since it has many built-in capabilities and packages that make it simple to address the demands of data science.
The fundamentals of Python programming, such as its data structures, functions, and classes, will be covered first. The course will next give a fundamental overview of linear regression from scratch, followed by an introduction to Numerical Python (NumPy).
We will introduce the fundamental concepts of calculus, linear algebra, and statistics along the way.
You will have the knowledge and skills necessary to effectively complete a demanding course in machine learning at the conclusion of this session.
