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• date: 21 March 2025
• posted by: voska89

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

What you'll learn

Understand the importance of high-quality data in AI & machine learning.
Apply data cleaning techniques to handle missing and poor-quality data.
Perform feature selection, scaling, and transformation for better model performance.
Work with categorical, numerical, text-based, and image features effectively.
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 preprocessing pipelines using custom transformers.
Visualize data using weighted scatter plots and shapefiles.
Understand and process image and geographic datasets for AI & machine learning applications.
Gain experience with traditional structured datasets, image datasets, and geographic datasets, providing a broader perspective on data used in AI & ML projects.
Enhance 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 preprocessing techniques using Scikit-learn, pandas, NumPy, and Matplotlib.

Requirements

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.

Description

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)

Who this course is for

Aspiring AI & Machine Learning Developers who want to master data preprocessing.
Data Scientists & Analysts looking to improve model accuracy and efficiency.
AI & ML Engineers working with real-world datasets, including geographic and image data.
Students & Researchers interested in learning advanced data preparation techniques.
Homepage:

https://www.udemy.com/course/data-preprocessing-for-machine-learning-and-data-analysis/

   
   
   

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