Build, Train & Deploy AI Machine Learning with Python

This course takes you beyond the basics. You will build a complete, production-ready text classification system from scratch—the kind of project that stands out in interviews and portfolios. Instead of using small toy datasets, you will work with 47,692 real social media posts, training a model that achieves over 81% accuracy in detecting cyberbullying-level toxicity, reflecting the scale and complexity used in real-world projects.You will not stop at a Jupyter notebook. The course walks you through the full end-to-end lifecycle: from raw, messy text data to a live, deployed web application that anyone can access through a public URL. Along the way, you will master essential data science skills: cleaning and preprocessing text, extracting TF‑IDF features, training and tuning classification models with scikit-learn, and evaluating performance with metrics that hiring managers recognise.You will then build an interactive dashboard with Streamlit, so users can submit text, see predictions, and view visualizations in real time—without needing to write HTML, CSS, or JavaScript. The app will be deployed to the cloud using free hosting, giving you a real link you can include on your CV, LinkedIn, or portfolio website.A key part of this course is ethical AI. Modern companies are increasingly focused on fairness, bias, and accountability in automated systems. You will learn how to detect potential bias in your model, evaluate performance across different subgroups, and design human-in-the-loop workflows that keep people in control of important decisions. You will also explore when AI should not be used, and how to communicate limitations clearly.By the end of the course, you will have a fully deployed application that analyzes thousands of texts with strong accuracy, complete with interactive charts and ethical safeguards. You will be able to say in interviews: “I built this production system. Here is the live demo. Here is the GitHub repository. Here is how I handled bias and interpretabili