Efficient Data Feeding and Labeling for Model Training
Creating data models using machine learning requires effective training data. This course will teach you how to feed your data model’s training process using data labeling for supervised training and unlabeled data for semi-supervised training.
What you'll learn
Machine learning data models are only as effective as their training data. In this course, Efficient Data Feeding and Labeling for Model Training, you’ll gain the ability to finalize the preparation of your training data and choose the most appropriate manner to feed it into your data model training. First, you’ll explore the meaning of data feeding and common techniques. Next, you’ll discover data labeling for supervised learning, followed by unlabeled data for semi-supervised learning. Finally, you’ll learn how to employ data labeling tools.
When you’re finished with this course, you’ll have the skills and knowledge of data labeling and feeding needed to train machine learning data models.
Table of contents
- Overview and Data Labeling 2m
- Supervised and Unsupervised Learning 2m
- Machine Learning (ML) Architectures for Supervised and Unsupervised Learning 2m
- Demo: TensorFlow Data Sets (TFDS) and Data Labels in Supervised Learning 2m
- Demo: Managing Unlabeled Data in Unsupervised Learning 2m
- Data Augmentation, Data Annotation, and Quality Control 4m
- Data Labeling Tools 2m
- Summary 2m
About the author
Internationally known, best-selling technology author, Dan Hermes inspires technologists to build successful AI projects and businesses to develop winning AI and mobile strategies. A Microsoft Regional Director (RD), Dan has advised on dozens of successful business apps in healthcare, retail, government, education, finance, transportation, biotech, and others. AI-driven projects include a Microsoft/Future Farmers of America (FFOA) partnership, visual recognition and generative proposals for Avan... more