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Level
Language
Rating
Category
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We found 9 courses available for you
Course Meta
2 Months
5

machine learning

  • 6 Lessons
  • 25 Students
Intermediate Level
Transfer Learning in Machine Learning
5
  • 6 Lessons
  • 2 Months
What You’ll Learn?
  • Comprehend the concept of transfer learning and its applications in the utilization of previously trained models for new tasks.
  • Learn how to fine-tune pre-trained models and extract transfer learning-relevant features.
  • Explore domain adaptation techniques and transfer learning across various data modalities.
Course Meta
2 Months
4.5

machine learning

  • 8 Lessons
  • 25 Students
Intermediate Level
Machine Learning for Image Processing
4.5
  • 8 Lessons
  • 2 Months
What You’ll Learn?
  • Enhance your ability to process and analyze visual data by studying techniques for extracting image features.
  • Learn how to construct image classification models and investigate object detection techniques for practical applications.
  • Master image segmentation algorithms and learn how to apply artistic styles to images through deep learning.
Course Meta
2 Months
5

machine learning

  • 6 Lessons
  • 25 Students
Advanced Level
Machine Learning for Anomaly Detection
5
  • 6 Lessons
  • 2 Months
What You’ll Learn?
  • Learn about numerous anomaly detection algorithms and how to identify anomalous data patterns.
  • Learn techniques for detecting anomalies in time series data, which are crucial for applications such as fraud detection.
  • Examine anomaly detection's real-world applications in industries such as manufacturing, finance, and cybersecurity.
Course Meta
2 Months
3.5

machine learning

  • 6 Lessons
  • 25 Students
Advanced Level
Ethical Considerations in Machine Learning
3.5
  • 6 Lessons
  • 2 Months
What You’ll Learn?
  • Learn the ethical challenges associated with bias in machine learning models and the strategies for ensuring impartiality.
  • Investigate the significance of privacy in machine learning, as well as methods for protecting sensitive data during training and deployment.
  • Explore the openness and responsibility of machine learning, confronting issues of model interpretability and accountable AI development.