| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.mp4 | 19.7 MB | ||
| 2 - Setting Up Your Development Environment | |||
| 10. Understanding Virtual Environments.mp4 | 11.6 MB | ||
| 11. Creating and Activating Virtual Environments.mp4 | 40.7 MB | ||
| 12. Updating Pip.mp4 | 10.3 MB | ||
| 13. Visual Studio Code Setup.mp4 | 67.6 MB | ||
| 14. Opening project in VS Code.mp4 | 7.4 MB | ||
| 3 - Google Colab Setup | |||
| 15. Creating Google account.mp4 | 70.9 MB | ||
| 16. Accessing Google Colab.mp4 | 103.6 MB | ||
| 17. Build a Python Dictionary and Pandas DataFrame.mp4 | 72.5 MB | ||
| 18. Create a simple Visualization.mp4 | 67.9 MB | ||
| 4 - Dataset Exploration | |||
| 19. Exploratory Data Analysis.mp4 | 60.3 MB | ||
| 20. Installing essential Python Libraries.mp4 | 9.1 MB | ||
| 21. Opening project directory in vs code.mp4 | 7.4 MB | ||
| 22. Importing Essential Python Libraries.mp4 | 41.6 MB | ||
| 23. Understanding Stop Words in NLP.mp4 | 57.8 MB | ||
| 5 - Text Preprocessing & Data Preparation | |||
| 24. Building the Classification System Architecture.mp4 | 73.9 MB | ||
| 25. Text Cleaning and Preprocessing.mp4 | 66.7 MB | ||
| 26. Building an Automated Data Pipeline.mp4 | 87.9 MB | ||
| 27. Feature Engineering - Vocabulary Analysis.mp4 | 67 MB | ||
| 6 - Model Training & Evaluation | |||
| 28. Training Our Machine Learning Model.mp4 | 119.5 MB | ||
| 29. Evaluating Model Performance.mp4 | 133 MB | ||
| 30. Analyzing Ambiguous Cases.mp4 | 96.5 MB | ||
| 31. Testing on Real-World Examples.mp4 | 127.8 MB | ||
| 7 - Generating Reports & Visualizations | |||
| 32. Generating Analysis Reports.mp4 | 64 MB | ||
| 33. Assembling the Complete System.mp4 | 111.8 MB | ||
| 34. Creating Professional Visualizations.mp4 | 97.6 MB | ||
| 8 - Building Interactive Dashboards With Streamlit | |||
| 35. Building an Interactive Dashboard.mp4 | 146.8 MB | ||
| 36. Running Your Streamlit Application.mp4 | 85.9 MB | ||
| 9 - Deploying To The Cloud | |||
| 37. Creating Github Account.mp4 | 63.1 MB | ||
| 38. Deploying to Streamlit Cloud.mp4 | 50.7 MB | ||
| 9. Installing Python on Windows.mp4 | 44.6 MB | ||
| 2. Course Roadmap & Learning Path.html | 1.9 KB | ||
| 3. Machine Learning Concepts Quick Reference.html | 7.5 KB | ||
| 4. Why Ethical AI Matters.html | 9.3 KB | ||
| 5. Evaluation Metrics Deep Dive.html | 11.7 KB | ||
| 6. What is Artificial Intelligence.mp4 | 132 MB | ||
| 7. Understanding AI Prompts.mp4 | 41.5 MB | ||
| 8. Download Project Code and Dataset.html | 1.1 KB | ||
| 8. Download Project Code and Dataset_Resource_cyberbullying-analysis.py | 18.2 KB | ||
| 8. Download Project Code and Dataset_Resource_streamlit-dashboard.py | 21.4 KB | ||
| 8. Download Project Code and Dataset_Resource_visualizations.py | 7.5 KB | ||
| cyberbullying_tweets.csv | 6.8 MB |
Machine Learning Project: Build & Deploy Real AI with Python
https://WebToolTip.com
Published 12/2025
Created by Bluelime Learning Solutions
MP4 | Video: h264, 1920x1080 | 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 nstrating 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
nstrates 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.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.2 GB | freecoursewb | 15 hours | 16 | 16 | |
| 1.9 GB | freecoursewb | 3 weeks | 3 | 74 | |
| 2.6 GB | freecoursewb | 3 weeks | 6 | 3 | |
| 2.5 GB | freecoursewb | 1 month | 1 | 9 | |
| 1.5 GB | freecoursewb | 2 months | 6 | 7 |
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