• Learners familiar with Python, TensorFlow, and Keras basics (e.g., Sequential models, layers)

• Machine learning students and practitioners who understand basic model-building and want to extend their skills to text and NLP tasks. Ideal for those who want to build their first text classification model.

Course Outline:

1. The Challenge with Text Data in ML/DL
   • Why text is hard: variable length, vocabulary size, semantics
   • The goal: turn text into numeric tensors for neural networks
2. Text Preprocessing: Tokenization, Padding, Vocabulary
   • Tokenization: building text into words or sub words
   • Vocabulary building: Assigning unique integers to tokens
   • Padding: Making sequences the same length
   • Hands-on: Using Tokenizer, pad sequences
3. Embedding Layers and Word Embeddings
   • Why embeddings? (dense vector representations of words)
   • Keras Embedding layer: Turning tokenized input into dense vectors
   • Hands-on: Add an embedding layer to a dummy model (inspect output shapes)
4. Using the Keras TextVectorization Layer
   • Overview: What is TextVectorization?
   • Preprocessing text
   • How it handles tokenization, vocabulary and sequence length
   • Hands-on: Use TextVectorization in the neural network

Training material provided: Yes (Digital format)

Hands-on Lab: Instructions will be provided to install Jupyter notebook and other required python libraries. Students can opt to use ‘Google Colaboratory’ if they do not want to install these tools