Sentiment Analysis and Text Classification with Movie Reviews

Understanding public opinion through text is a powerful skill in modern data science. Analyzing movie reviews offers the perfect playground to learn how machine learning algorithms read, interpret, and classify human sentiment. This text-only course guides you through the essential concepts of Natural Language Processing (NLP) and text classification. You will transition from understanding raw text data to building and evaluating your own sentiment analysis models using Python. What you'll learn: Understand foundational NLP concepts, including tokenization, stop-word removal, and text normalization; Convert text data into numerical features using techniques like bag-of-words and TF-IDF; Build a sentiment classification model using Python and standard machine learning libraries; Evaluate model performance using key metrics like accuracy, precision, recall, and confusion matrices; Apply modern Python coding standards, including type hints, to structure your data analysis pipelines; Explore how modern transformer-based models compare to traditional classification techniques. The curriculum begins with core definitions and text preprocessing theory before moving step-by-step into feature extraction, model training, and performance evaluation using real-world movie review datasets. This course is designed for absolute beginners in data science and Python programming, with no prior machine learning experience required. Start reading today to unlock the power of text classification.