Published 12/2025
Created by Bluelime Learning Solutions
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 38 Lectures ( 3h 35m ) | Size: 2.2 GB
Train text classifier on 47K samples, detect AI bias, create Streamlit dashboards & deploy to cloud – ethically
What you’ll learn
Build complete machine learning classification systems from scratch using Python and scikit-learn
Train text classification models on 47,692+ real-world samples, achieving 80%+ accuracy with NLP
Implement advanced text preprocessing: tokenization, stop words, anonymization, and TF-IDF features
Evaluate models with industry-standard metrics (accuracy, precision, recall, F1, confusion matrices)
Create interactive web dashboards using Streamlit that display real-time predictions and visualizations
Deploy ML applications to the cloud FREE using Streamlit Cloud with shareable public URLs
Work with NumPy, Pandas, Matplotlib, and Seaborn for data analysis and professional visualizations
Design automated data pipelines that clean and prepare text data for machine learning at scale
Detect and mitigate bias in AI systems using fairness-aware evaluation strategies
Apply ethical AI principles: human-in-the-loop design, transparency, and accountability frameworks
Explain ML predictions to non-technical stakeholders using interpretable models and visualizations
Identify when AI should and shouldn’t be used, understanding ethical implications of automation
Build a portfolio-ready detection system demonstrating real-world problem-solving
Deploy production-ready ML apps with documentation, Git/GitHub version control, and cloud hosting
Generate professional reports and visualizations that communicate technical results effectively
Create reproducible ML workflows with proper code organization and dependency management
Present work professionally through GitHub repos
Understand the complete data science workflow from problem definition through deployment
Apply NLP techniques to various text classification problems: spam, sentiment, content moderation
Demonstrates most in-demand skills: ethical AI, bias detection, interpretability, deployment
Requirements
Basic Python Programming.
Willingness to learn
Computer (Windows, Mac, or Linux)
Internet Connection
Required Software will be covered in the course.
Description
Are you tired of machine learning tutorials that stop at theory? Ready to build something real that you can actually show employers?This course takes you beyond the basics. You’ll build a complete, production-ready text classification system from scratch—the kind of project that gets you hired.Here’s what makes this different: You won’t work with toy datasets like Titanic or Iris. Instead, you’ll train a machine learning model on 47,692 real social media posts, achieving over 81% accuracy in detecting cyberbullying. This is the scale and complexity employers expect.But we don’t stop at training. Most courses teach you to build models in Jupyter notebooks, then leave you wondering “now what?” This course shows you the complete workflow—from raw data to a live, deployed application anyone can access on the internet.You’ll master the essential skills data scientists use every day: preprocessing messy text data, extracting meaningful features with TF-IDF, training classification models with scikit-learn, and evaluating performance with industry-standard metrics. You’ll work with Python libraries including NumPy, Pandas, Matplotlib, and Seaborn to analyze data and create professional visualizations.Then comes the part that separates you from other candidates: deployment. You’ll build an interactive web dashboard using Streamlit—no HTML, CSS, or JavaScript required—and deploy it to the cloud completely free. Your application will have a real URL you can share in job interviews and include in your portfolio.What truly sets this course apart is our focus on ethical AI. In 2025, companies aren’t just looking for people who can build AI—they need people who can build it responsibly. You’ll learn to detect and mitigate bias in machine learning systems, design human-in-the-loop workflows, and make AI decisions transparent and accountable. These are the skills that make you invaluable.This isn’t just another course—it’s your bridge from Python developer to AI/ML engineer. Whether you’re a software developer adding ML to your toolkit, an aspiring data scientist building your portfolio, or a career changer proving you can do technical work, this project demonstrates end-to-end competency.By the end, you’ll have something concrete to show: a deployed application analyzing thousands of texts with high accuracy, complete with interactive visualizations and ethical safeguards. You’ll be able to say in interviews: “I built this production system. Here’s the live demo. Here’s the code on GitHub.”All tools are free. All code is provided. All concepts are explained clearly without confusing jargon
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