Natural Language Processing in TensorFlow Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to Natural Language Processing using TensorFlow, designed for learners with foundational machine learning knowledge. You'll explore key NLP concepts through hands-on projects, including sentiment analysis, word embeddings, and sequence models like RNNs, GRUs, and LSTMs. The course blends theory with practical implementation, culminating in a final project where you generate original text using trained models. With approximately 23 hours of content, the flexible structure is ideal for working professionals aiming to advance in AI-related roles.
Module 1: Sentiment in Text
Estimated time: 5 hours
- Introduction to sentiment analysis
- Binary classification of text data
- Building neural networks for sentiment detection
- Evaluating model performance on text
Module 2: Word Embeddings
Estimated time: 6 hours
- Representing words as vectors
- Understanding semantic meaning in embeddings
- Using TensorFlow for embedding layers
- Visualizing and interpreting word vectors
Module 3: Sequence Models
Estimated time: 6 hours
- Introduction to RNNs, GRUs, and LSTMs
- Handling sequential data with recurrent networks
- Implementing sequence models in TensorFlow
- Training models on variable-length sequences
Module 4: Sequence Models and Literature
Estimated time: 6 hours
- Text generation with LSTMs
- Training models on literary datasets
- Generating original poetry and prose
- Adjusting model parameters for creative output
Module 5: Final Project
Estimated time: 6 hours
- Design and train a custom NLP model
- Apply learned techniques to real-world text data
- Submit generated text or classification results
Prerequisites
- Proficiency in Python programming
- Familiarity with basic machine learning concepts
- Understanding of neural networks fundamentals
What You'll Be Able to Do After
- Build natural language processing systems using TensorFlow
- Analyze sentiment in text data with neural networks
- Represent sentences as vectors using word embeddings
- Apply RNNs, GRUs, and LSTMs to sequential text problems
- Generate creative text such as poetry using trained LSTMs