Deep Learning for NLP: Word Embeddings and Text Classification in Python

Text data is everywhere, but teaching computers to understand human language requires translating words into a language machines speak: numbers. This course guides you through the foundational neural network architectures that revolutionized how computers process text. You will transition from basic text-processing techniques to building deep learning models that capture the semantic meaning of words. Through clear written explanations and structured Python code examples, you will learn how to represent text as dense vectors, perform sentiment analysis, and sequence-tag text data. What you'll learn: - Understand the core mathematical concepts behind word embeddings, vector spaces, and semantic similarity. - Implement classic word representation models including word2vec and GloVe from first principles. - Build text classification and sentiment analysis models using recurrent neural networks (RNNs) in Python. - Apply the Gensim library to load pre-trained word vectors and solve semantic analogy problems. - Explore sequence labeling tasks like parts-of-speech tagging and named entity recognition. - Learn modern NLP foundations, including subword tokenization and the basic mechanics of attention layers. The journey begins with fundamental NLP terminology and mathematical concepts, progressing from static bag-of-words representations to dynamic word embeddings. You will then explore sequential neural network architectures, studying how models process text chronologically to perform classification and sequence tagging. This course is designed for beginner-to-intermediate programmers, data enthusiasts, and aspiring AI developers who want a solid conceptual and practical foundation in neural NLP. Basic familiarity with Python and algebra is recommended, but no prior deep learning experience is required. Start reading today to unlock the power of deep learning for text processing.