Applied NLP in Python: Transformers, CNNs, and Text Classification

Text data is expanding rapidly, and modern organizations rely on natural language processing to extract insights, automate translation, and understand sentiment. This course teaches you how to design and implement modern NLP models using Python and TensorFlow. You will transition from understanding basic text preprocessing to implementing deep learning architectures. By reading clear explanations and studying production-ready code snippets, you will gain the skills to tackle text classification, sequence-to-sequence translation, and modern vector embedding workflows. What you'll learn: - Understand foundational NLP concepts, text tokenization, and vocabulary building - Build convolutional neural networks optimized for text classification and sentiment analysis - Implement sequence-to-sequence models using modern Transformer architectures - Apply pre-trained models and explore the basics of modern vector embeddings - Configure training pipelines in cloud-based notebook environments using TensorFlow - Practice debugging text processing pipelines through structured code analysis The course starts with essential terminology and text preprocessing techniques before advancing to neural network construction. You will progress through step-by-step written walkthroughs covering sentiment analysis, machine translation, and modern transformer-based workflows. This course is designed for beginners in machine learning and Python developers who want to specialize in text processing. No prior NLP experience is required. Start reading today to build your foundation in modern natural language processing.