Applied Machine Learning: NLP and Flask Web App Deployment

Unlock the power of text data and learn how to bring your machine learning models to life on the web. This text-based course guides you from foundational machine learning concepts to building and deploying interactive text-processing applications. You will transition from writing basic Python scripts to developing complete NLP pipelines and deploying them as functional web services. Through clear written explanations, structured code walkthroughs, and practical exercises, you will master the core techniques of natural language processing and web integration. What you'll learn: 1) Understand foundational machine learning concepts, NLP terminology, and essential text-preprocessing workflows. 2) Clean and prepare text data using regular expressions, tokenization, stop-word removal, and lemmatization. 3) Build and evaluate sentiment analysis models using Naive Bayes, TextBlob, and Vader. 4) Develop a content-based movie recommendation engine from scratch using text similarity metrics. 5) Configure and build lightweight web applications using the Flask framework to serve your models. 6) Apply modern Python packaging and clean coding standards to ensure your machine learning code is production-ready. The course begins with essential definitions and text-processing fundamentals before moving into sentiment classification and recommendation algorithms. Finally, you will explore how to wrap these models in a Flask web application and structure your code using modern Python best practices. This course is designed for aspiring data scientists, web developers, and programming beginners who want to learn how to deploy machine learning models. No prior experience with NLP or Flask is required, though a basic familiarity with Python is helpful. Start reading today to build and deploy your first text-analysis web applications.