📅 2023-05-02 — Session: Developed exercises for unstructured data processing in Python
🕒 20:25–20:35
🏷️ Labels: Python, Data Processing, Unstructured Data, Exercises, Machine Learning
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal: The session aimed to develop and outline exercises for processing unstructured data using Python, focusing on both text and image data.
Key Activities:
- Reviewed fundamental data processing techniques, including data manipulation, aggregation, visualization, and storage using SQL and NoSQL databases.
- Developed exercises for loading and processing unstructured data types, such as text files, images, and audio files, using Python libraries like Pillow, Librosa, and requests.
- Designed practical exercises for text processing and analysis using spaCy, NLTK, and TextBlob, covering tokenization and sentiment analysis.
- Implemented image processing exercises using OpenCV, including feature extraction and classification using convolutional neural networks with TensorFlow.
Achievements:
- Successfully created a comprehensive set of exercises for students to practice unstructured data handling and processing in Python.
Pending Tasks:
- Further refinement of exercises to include more advanced machine learning techniques for image and text data.