Data Cleansing Master Class in Python

This guide is aimed at individuals who want to become machine-learning engineers. The importance of data preparation in machine learning projects is emphasized, as it is essential for transforming raw data into a suitable format for modeling. The course covers various topics, such as data imputation, advanced techniques for data cleansing, and real-world data cleaning techniques. It also highlights the significance of avoiding data leakage and ensuring accurate model evaluation through proper data preparation.

To meet the expectations of machine learning algorithms, which typically require numerical input data, it may be necessary to convert non-numeric data types or values such as labels. Different algorithms have specific requirements regarding data types, scales, probability distributions, and relationships among input variables. Therefore, modifying the data to meet these expectations may be necessary.

This course is ideal for those serious about pursuing a career as a machine-learning engineer in the real world. It assumes a solid understanding of Python and the basics of machine learning, as well as familiarity with machine learning libraries. By completing this course, individuals will gain expertise in data cleaning and preprocessing, which are crucial skills for successful machine learning projects.