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 scalable approaches to data labeling for machine learning projects.

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.

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.

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 module provides hands-on insights into applying these techniques using Python.

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.

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 module emphasizes practical approaches for working with limited labeled data.

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-time video processing and motion analysis.

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 examples.

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 in working with audio data.

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.

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.