Build, Train & Deploy AI NLP Text‑Based ML Models

[OneDDL]

Free Download Build, Train & Deploy AI NLP Text‑Based ML Models
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.35 GB | Duration: 3h 50m
Build and deploy Portfolio‑Ready ML Apps with Python ,NLP, AI Tools & Streamlit.

What you'll learn
Build machine learning classification systems from scratch using Python and scikit-learn
Train NLP text classifiers on 47,692 real-world samples with strong evaluation metrics
Design full preprocessing pipelines: tokenization, stop words, anonymization, TF-IDF
Evaluate performance with accuracy, precision, recall, F1 score, confusion matrices
Evaluate performance with accuracy, precision, recall, F1 score, confusion matrices
Create interactive real-time dashboards using Streamlit web apps
Deploy ML applications to the cloud with shareable public URLs
Analyze data effectively using NumPy, Pandas, Matplotlib, and Seaborn
Detect and mitigate AI bias using fairness-aware evaluation
Apply responsible and explainable AI practices with transparency and accountability
Present model results clearly through visualizations and stakeholder-friendly reporting
Build a portfolio-ready toxic content detection system with GitHub documentation
Understand the complete ML lifecycle from data to deployment and monitoring
Apply NLP classification to practical use cases like sentiment analysis and moderation
Requirements
Basic Python programming knowledge (variables, functions, lists)
A computer (Windows, Mac, or Linux)
Internet connection
No prior machine learning experience required
All necessary software and tools are installed step-by-step during the course
Description
Most machine learning tutorials stop at theory or leave you with a model that never leaves a notebook. This project-based course goes all the way to production deployment.You will build a real AI solution that detects cyberbullying-level toxicity in social media text. Instead of small toy datasets, you will train on 47,692 real posts, reflecting the scale and complexity used in real projects.You will:Clean and structure messy text data for modelingEngineer TF-IDF features and train classification modelsEvaluate model performance and improve reliabilityDetect and address bias using responsible AI techniquesBuild an interactive web app with StreamlitDeploy the system to the cloud with a live public URLAt the end, you will have:A fully deployed machine learning applicationA professional GitHub repositoryVisualizations and reports explaining your resultsPractical experience in ethical, interpretable, deployable AIThis course prepares you to discuss your work confidently in job interviews:"I built a production system. Here is the live demo. Here's the code. Here's how I managed fairness and explainability."All tools are free, all code is provided, and every concept is explained clearly.Who This Course Is ForPython developers transitioning into machine learning or AI engineeringAspiring data scientists seeking strong portfolio projectsCareer changers gaining practical, job-focused ML experienceStudents and graduates wanting to go beyond theoretical practiceSoftware engineers adding ML deployment skills to their workflowProduct managers, analysts, UX professionals working with AI teamsEntrepreneurs building AI-enabled applications or prototypesAnyone interested in applying AI responsibly-beyond just accuracy
Python developers transitioning into machine learning or AI engineering,Aspiring data scientists seeking strong portfolio projects,Career changers gaining practical, job-focused ML experience,Students and graduates wanting to go beyond theoretical practice,Software engineers adding ML deployment skills to their workflow,Product managers, analysts, UX professionals working with AI teams,Entrepreneurs building AI-enabled applications or prototypes,Anyone interested in applying AI responsibly-beyond just accuracy
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