What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
Article
This course is part of Applied Machine Learning Specialization
Instructor: Erhan Guven
Included with
Recommended experience
Intermediate level
Experience in machine learning concepts, data analysis, and comfort with linear algebra and algorithms.
Recommended experience
Intermediate level
Experience in machine learning concepts, data analysis, and comfort with linear algebra and algorithms.
Understand and implement machine learning techniques for computer vision tasks, including image recognition and object detection.
Analyze data features and evaluate machine learning model performance using appropriate metrics and evaluation techniques.
Apply data pre-processing methods to clean, transform, and prepare data for effective machine learning model training.
Implement and optimize supervised learning algorithms for classification and regression tasks.
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September 2024
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The course "Applied Machine Learning: Techniques and Applications" focuses on the practical use of machine learning across various domains, particularly in computer vision, data feature analysis, and model evaluation. Learners will gain hands-on experience with key techniques, such as image processing and supervised learning methods while mastering essential skills in data pre-processing and model evaluation.
This course stands out for its balance between foundational concepts and real-world applications, giving learners the opportunity to work with widely-used datasets and tools like scikit-learn. Topics include image classification, object detection, feature extraction, and the selection of evaluation metrics for assessing model performance. By completing this course, learners will be equipped with the practical skills necessary to implement machine learning solutions, enabling them to apply these techniques to solve complex problems in data processing, computer vision, and more.
Explore the practical applications of machine learning through hands-on modules covering data pre-processing, feature extraction, model evaluation, and supervised learning techniques. Delve into specialized topics such as computer vision and learn to implement and assess various machine learning models. This course combines theoretical insights with practical lab activities to equip you with essential skills in applied machine learning.
2 readings
Discover the foundational principles and practical applications of machine learning in the field of computer vision. This module covers essential concepts, including data preprocessing, dataset management, classification techniques, and model evaluation, providing a comprehensive introduction to applying machine learning to visual data.
5 videos2 readings3 assignments1 ungraded lab
Explore essential techniques in data feature analysis and model evaluation critical to effective machine learning applications. Learn to identify, preprocess, and integrate datasets from diverse sources like UCI KDD and Kaggle. Gain hands-on experience with the Weka framework for data preprocessing and classification, and understand evaluation metrics including Receiver Operating Characteristic curves. By the end of this module, you'll grasp the nuances of model overfitting and strategies to optimize model performance.
7 videos2 readings3 assignments1 ungraded lab
Master the essential techniques of data pre-processing to enhance machine learning model performance. This module covers the foundational aspects of data cleaning, various data formats, and processing methods. You'll delve into advanced topics like discretization, data transformation, and reduction techniques. By the end of this module, you'll be adept at engineering data features, applying feature selection, and refining datasets for optimal machine learning outcomes.
5 videos1 reading3 assignments1 ungraded lab
Delve into the core principles and mathematical foundations of supervised learning algorithms. This module covers essential techniques, including the Perceptron algorithm, Naive Bayes classifier, and Linear Regression methods. You'll gain practical experience implementing and visualizing these algorithms, and explore how classifier decision boundaries shift with parameter changes. Additionally, learn to apply text classification using real-world datasets for hands-on understanding of supervised learning applications.
6 videos2 readings3 assignments1 programming assignment
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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