Udemy – Data Preprocessing for Machine Learning and Data Analysis 2025-3

Udemy – Data Preprocessing for Machine Learning and Data Analysis 2025-3 Downloadly IRSpace

Aug 17, 2025 - 02:35 Updated: Aug 19, 2025 - 14:30

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Udemy – Data Preprocessing for Machine Learning and Data Analysis 2025-3

Data Preprocessing for Machine Learning and Data Analysis. This course is a comprehensive guide to data preparation, covering key techniques and practical applications on a variety of data types. The course includes 29 downloadable files, consisting of a 91-page PDF summary and 28 related Python code files. In a classic “classroom-style” approach, the theoretical foundations of each topic are carefully explained, including the rationale for using the techniques, their applications, and their benefits. Then, with a focus on coding, line-by-line Python examples are provided that can be adapted for different projects. Data preprocessing is a critical step in AI that directly impacts model performance, and this course covers essential techniques such as missing value handling, scaling, batch data encoding, feature engineering, and dimensionality reduction (PCA). It also teaches geographic data visualization methods and weighted scatter plots for spatial AI applications. In addition to structured data, the course includes image and geographic data to provide a comprehensive understanding of real-world projects. By the end of the course, learners will be able to build automated data preprocessing pipelines and prepare datasets for machine learning and deep learning. This course is designed for machine learning engineers, data scientists, AI developers, and researchers, equipping them with practical skills and best practices to improve data quality and model performance.

What you will learn

Understand the importance of high-quality data in artificial intelligence and machine learning.
Employ data cleansing techniques to manage missing and poor-quality data.
Perform feature selection, scaling, and transformation for better model performance.
Work effectively with batch, numeric, text, and image features.
Identify correlations and use visualization techniques to gain insights.
Implement Principal Component Analysis (PCA) for dimensionality reduction.
Properly split datasets for training, testing, and cross-validation.
Build automated data preparation pipelines using custom transformers.
Data visualization using weighted scatter plots and shapefiles.
Understanding and processing image and geographic datasets for artificial intelligence and machine learning applications.
Gain experience with traditional structured datasets, image datasets, and geographic datasets, which provides a broader perspective on the data used in AI and machine learning projects.
Boost your resume with in-demand data science skills, including statistical analysis, Python with NumPy, pandas, Matplotlib, and advanced statistical analysis.
Learn and apply useful data preparation techniques using Scikit-learn, pandas, NumPy, and Matplotlib.

This course is suitable for people who:

Aspiring AI and machine learning developers who want to master data preparation.
Data scientists and analysts looking to improve model accuracy and efficiency.
Artificial intelligence and machine learning engineers who work with real-world datasets, including geographic and image data.
Students and researchers interested in learning advanced data preparation techniques.

Course details for Data Preprocessing for Machine Learning and Data Analysis

Publisher: Udemy
Instructor: Muhtar Qong
Training level: Beginner to advanced
Training duration: 8 hours and 19 minutes
Number of lessons: 28

Course topics

Prerequisites for the Data Preprocessing for Machine Learning and Data Analysis course

There are no special requirements for this course. If you have beginner to intermediate-level Python experience, that is enough to follow along and understand the concepts. This course follows a classic classroom-style approach, where we first cover the theoretical foundations before moving on to hands-on coding sessions. This structured format makes the course easy to understand for learners at all levels.