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Build, train & deploy AI NLP text‑based ML models.
2 students

Build, train & deploy AI NLP text‑based ML models.

Build and deploy Portfolio‑Ready ML Apps with Python ,NLP, AI Tools & Streamlit.
Created bySkill Tree
Last updated 3/2026
English

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

Course content

9 sections • 42 lectures • 3h 51m total length

  • Introduction2:45
  • What is Machine Learning0:53
  • What is AI10:34
  • Understanding Prompt5:40
  • What is NLP0:33
  • Download Project Code and Dataset0:01

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:

  1. Clean and structure messy text data for modeling

  2. Engineer TF-IDF features and train classification models

  3. Evaluate model performance and improve reliability

  4. Detect and address bias using responsible AI techniques

  5. Build an interactive web app with Streamlit

  6. Deploy the system to the cloud with a live public URL

At the end, you will have:

  • A fully deployed machine learning application

  • A professional GitHub repository

  • Visualizations and reports explaining your results

  • Practical experience in ethical, interpretable, deployable AI

This 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 For

  • 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

Who this course is for:

  • 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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