Data Preprocessing for Machine Learning and Data Analysis

Data Preprocessing for Machine Learning and Data Analysis free download

A Comprehensive Guide for AI & Machine Learning Developers and Data Scientists

This course includes 29 downloadable files, including one PDF file containing the entire course summary (91 pages) and 28 Python code files attached to their corresponding lectures.

If we understand a concept well theoretically, only then can we apply it effectively for our purposes. Therefore, this course is structured in a classic "classroom-style" approach. First, we dedicate sufficient time to explaining the theoretical foundations of each topic, including why we use a particular technique, where it is applicable, and its advantages.

After establishing a solid theoretical understanding, we move on to the coding session, where we explain the example code line by line. This course includes numerous Python-based coding examples, and for some topics, we provide multiple examples to reinforce understanding. These examples are adaptable, meaning you can modify them slightly to fit your specific projects.

Data preprocessing is a crucial step in AI and machine learning, directly affecting model performance, accuracy, and efficiency. Since raw data is often messy and unstructured, preprocessing ensures clean, optimized datasets for better predictions.

This hands-on course covers essential techniques, including handling missing values, scaling, encoding categorical data, feature engineering, and dimensionality reduction (PCA). We will also explore data visualization with geographic information, weighted scatter plots, and shapefiles, particularly useful for geospatial AI applications.

Beyond traditional structured datasets, this course includes image and geographic datasets, giving learners a broader perspective on real-world AI projects.

By the end, you’ll be able to build automated data preprocessing pipelines and prepare datasets efficiently for machine learning and deep learning applications.

Ideal for ML engineers, data scientists, AI developers, and researchers, this course equips you with practical skills and best practices for high-quality, well-processed datasets that enhance model performance.

You can download the entire course summary PDF from the final lecture (Lecture 28)