This free image processing course starts by introducing you to Digital Image Processing and computer vision. You will also learn about data augmentation, weight initialization, and regularization. Comprehend image processing using neural networks, image classification, and work on projects like smile and face detection, converting the image to a grey scale image, and converting the image to six different forms. Enroll in this free Digital Image Processing course and gain a certificate.
Essential Techniques in Machine Learning
This free image processing course starts by introducing you to Digital Image Processing and computer vision. You will also learn about data augmentation, weight initialization, and regularization. Comprehend image processing using neural networks, image classification, and work on projects like smile and face detection, converting the image to a grey scale image, and converting the image to six different forms. Enroll in this free Digital Image Processing course and gain a certificate.
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No. of Enrollments
0 students
LevelBeginner
CommitmentNot specified
Course Load3 Modules
PriceFree
About this course
This free image processing course starts by introducing you to Digital Image Processing and computer vision. You will also learn about data augmentation, weight initialization, and regularization. Comprehend image processing using neural networks, image classification, and work on projects like smile and face detection, converting the image to a grey scale image, and converting the image to six different forms. Enroll in this free Digital Image Processing course and gain a certificate.
Course Modules
Module 1
Module 1: Introduction to Data Science
In this module, you will learn about data science, terminologies and concepts in data science;data privacy in data science; data ethics in data science, artificial intelligence and machine learning; setting up the Python programming environment using different integrated development environments like Pycharm, Juypter notebook and Google Colab. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
What's included
Module 2
Module 2: Machine Learning
In this module, you will learn about machine learning, the key concepts, terminologies, types of machine learning techniques; machine learning pipeline, its applications and limitations;understand the required mathematics and statistics concepts for data science; and Python programming skills for data science. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
What's included
Module 3
Module 3: Machine Learning Applications
In this module, you will learn about the steps in machine learning applications for the supervised learning and unsupervised learning; the application of regression model for supervised learning; the application of classification using random forest for supervised learning; and the K-means clustering for unsupervised learning. Further, the module provides examples, exercises and problems for self-assessment in the learning process.
What's included
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About the instructor
Dr John DAUDI
I am Dr. John Daudi, a Professor of Electrical Engineering with a deep passion for embedded systems. I specialize in designing and developing innovative solutions that integrate hardware and software, bridging the gap between theory and practical applications. My work focuses on advancing embedded technologies and mentoring the next generation of engineers in both academic and research settings.