Data Labeling in Machine Learning with Python

Overview

Syllabus 12

1. Exploring Data for Machine Learning 1 hour

   This module guides learners through essential data exploration techniques, including statistical analysis and visualization using Python libraries. It covers the importance of data labeling and the role of exploratory data analysis (EDA) in preparing data for machine learning. Learners will gain pr…

2. Labeling Data for Classification 1 hour

   This module covers techniques for programmatically labeling data using tools like Snorkel, Compose, and K-means clustering. Learners will gain skills in creating labeling functions, applying business rules, and leveraging unsupervised methods for data preparation. The focus is on efficient and scal…

3. Labeling Data for Regression 1 hour

   This module covers techniques for labeling regression data when labeled data is limited, including semi-supervised learning, data augmentation, and K-means clustering. Learners will gain practical skills in using Python libraries to generate accurate labels for real-world applications.

4. Exploring Image Data 1 hour

   This module covers essential techniques for working with image data, including visualization using Python libraries, analyzing image properties, applying transformations for data augmentation, and understanding the impact of preprocessing steps on model performance.

5. Labeling Image Data Using Rules 1 hour

   This module covers techniques for labeling image data using rules, transformations, and transfer learning. Learners will explore methods to classify images based on visual features, color distribution, and object properties, with a focus on practical applications like plant disease detection. The m…

6. Labeling Image Data Using Data Augmentation 1 hour

   This module covers techniques for labeling image data using data augmentation, including training SVMs and CNNs with augmented datasets. Learners will gain practical skills in implementing these methods using Python and Keras, enhancing model generalization and accuracy.

7. Labeling Text Data 1.5 hours

   This module provides an in-depth exploration of text data labeling techniques using generative AI, Snorkel, and k-means clustering. Learners will gain hands-on experience with tools like NLTK and Azure OpenAI, and develop skills in text classification, summarization, and sentiment analysis. The mod…

8. Exploring Video Data 1 hour

   This module covers the fundamentals of working with video data using Python libraries such as cv2 and Matplotlib. Learners will gain skills in loading, extracting, and visualizing video frames, as well as applying clustering techniques for analysis. The course also introduces basic concepts in real…

9. Labeling Video Data 1 hour

   This module provides an in-depth exploration of video data labeling techniques using Python, including CNNs, autoencoders, and the Watershed algorithm. Learners will gain practical skills in building models, implementing transfer learning, and evaluating segmentation performance through real-world…

10. Exploring Audio Data 1.5 hours

    This module provides an in-depth exploration of audio data analysis, covering essential techniques for loading, visualizing, and extracting features from audio signals. Learners will gain hands-on experience with tools like Librosa and Matplotlib, and understand the ethical considerations involved…

11. Labeling Audio Data 1 hour

    This module explores the fundamentals of audio data labeling, including real-time audio capture, transcription using the Whisper model, and classification with CNNs and Hugging Face Transformers. Learners will gain hands-on experience with audio augmentation techniques to improve model robustness.

12. Hands-On Exploring Data Labeling Tools 2 hours

    This module provides hands-on experience with data labeling tools such as Azure Machine Learning, Label Studio, and CVAT. Learners will explore various annotation methods for images, text, video, and audio, and understand how to apply these tools to improve model accuracy and efficiency.

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Taught by

• Packt - Course Instructors