Applied Machine Learning: Techniques and Applications
Master practical machine learning techniques with focus on computer vision, data preprocessing, and model evaluation using industry tools.
Master practical machine learning techniques with focus on computer vision, data preprocessing, and model evaluation using industry tools.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Machine Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Implement machine learning techniques for computer vision tasks
Analyze data features and evaluate model performance
Apply effective data pre-processing methods
Develop supervised learning algorithms
Optimize model performance using evaluation metrics
Skills you'll gain
This course includes:
9.5 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course focuses on practical applications of machine learning, particularly in computer vision and data analysis. Students learn essential techniques in data preprocessing, feature engineering, and model evaluation. The curriculum covers supervised learning algorithms, implementation of computer vision tasks, and hands-on experience with tools like Weka. Special emphasis is placed on real-world applications and practical implementation of machine learning concepts.
Course Introduction
Module 1 · 10 Minutes to complete
Application of Machine Learning in Computer Vision
Module 2 · 3 Hours to complete
Data Features & Model Evaluation
Module 3 · 4 Hours to complete
Data Pre-Processing
Module 4 · 3 Hours to complete
Supervised Learning
Module 5 · 6 Hours to complete
Fee Structure
Instructor
Innovator in Cybersecurity and Machine Learning at Johns Hopkins University
Dr. Erhan Guven is a distinguished faculty member at Johns Hopkins University, where he teaches courses in machine learning, generative AI, natural language processing (NLP), graph analytics, and formal methods. He holds a Ph.D. in Computer Science from The George Washington University and has made significant contributions to the fields of cybersecurity and disease forecasting through his extensive research and numerous publications. Dr. Guven is also an inventor of several patents related to speech emotion detection and Voice over Internet Protocol (VoIP) technologies, showcasing his commitment to advancing technology in practical applications. His interdisciplinary approach combines theoretical knowledge with real-world problem-solving, making him a valuable asset to both the academic community and the broader field of computer science. Through his teaching and research, Dr. Guven continues to inspire students and contribute to cutting-edge advancements in technology.
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Frequently asked questions
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