DDS-8536

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
https://www.deeplearningbook.org/
Chapter 12 covers Sequence Modeling, including techniques for processing sequential data such as text, making it highly relevant for Natural Language Processing (NLP) tasks. This chapter is essential for understanding how recurrent neural networks (RNNs) and other deep learning models are used for sentiment analysis and text generation. Students should review this to learn the fundamentals of neural networks in the context of language modeling.

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow
Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (2nd ed.). O'Reilly Media.
https://learning.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Chapter 16 introduces Natural Language Processing (NLP) using machine learning techniques. The practical focus on using Scikit-Learn and TensorFlow to perform sentiment analysis will guide students through implementing text-based algorithms in Python. This chapter provides hands-on experience, making it a vital resource for completing sentiment analysis tasks in this module.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Chapter 13 on Hidden Markov Models (HMMs) and Sequence Models is directly applicable to NLP, where sequence models are often used to analyze language patterns in text. Students should review this chapter to understand the mathematical background behind text analysis algorithms, such as sentiment analysis and speech recognition.

Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow (3rd ed.). Packt Publishing.
https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/?sso_link=yes&sso_link_from=nationaluniversity
Chapter 8 covers working with text data and provides practical examples of how to implement NLP techniques using Python libraries. This resource is helpful for students who want to dive deeper into the practical coding aspects of NLP and sentiment analysis.