Natural Language Processing with Python: SpaCy, NLTK, and Deep Learning

Text data is everywhere, but turning raw, unstructured text into actionable insights requires specialized techniques. This written course guides you through the foundational concepts of Natural Language Processing (NLP) using Python, taking you from raw text to intelligent classification models. You will progress from understanding basic text manipulation to deploying machine learning and deep learning models for real-world tasks. By working through clear written explanations and structured code examples, you will learn how to clean text, extract key features, and build classification pipelines for sentiment analysis, spam detection, and information extraction. What you'll learn: - Clean and preprocess raw text data using NLTK and SpaCy to prepare it for machine learning algorithms. - Convert text into numerical representations using techniques like TF-IDF, Word2Vec, GloVe, and modern semantic embeddings. - Build and evaluate text classification models for sentiment analysis, spam detection, and document categorization using Scikit-Learn. - Implement deep learning architectures, including LSTMs, to capture sequential patterns in text data. - Extract structured information such as entities and keywords for practical applications like resume parsing. - Understand how modern transformer-based models build upon foundational NLP techniques. The course begins with core text processing and Python fundamentals before advancing to machine learning pipelines and deep learning architectures. You will explore practical text classification workflows step-by-step, ensuring a solid grasp of both theory and implementation. Designed for beginners with a basic interest in Python, this course requires no prior experience in data science or machine learning. Start reading today to unlock the power of text data and build your first NLP models.