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.
Applied Machine Learning: Techniques and Applications
Ce cours fait partie de Spécialisation Applied Machine Learning
Instructeur : Erhan Guven
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Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Data Pre-Processing
- Catégorie : Feature Engineering
- Catégorie : Supervised Learning
- Catégorie : Practical Application
- Catégorie : Model Evaluation
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Il y a 5 modules dans ce cours
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.
Inclus
2 lectures
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.
Inclus
5 vidéos2 lectures3 devoirs1 laboratoire non noté
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.
Inclus
7 vidéos2 lectures3 devoirs1 laboratoire non noté
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.
Inclus
5 vidéos1 lecture3 devoirs1 laboratoire non noté
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.
Inclus
6 vidéos2 lectures3 devoirs1 devoir de programmation
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