Delete directory 'data'

data/course_meta.json

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-{
-  "schedule": [
-    {
-      "date": "2026-09-01",
-      "lecture": "Lecture 1",
-      "topic": "Introduction to ML",
-      "location": "Room 101"
-    },
-    {
-      "date": "2026-09-08",
-      "lecture": "Lecture 2",
-      "topic": "Data Preprocessing",
-      "location": "Room 102"
-    },
-    {
-      "date": "2026-09-15",
-      "lecture": "Lecture 3",
-      "topic": "Linear Regression",
-      "location": "Room 103"
-    }
-  ],
-  "instructor": {
-    "name": "Dr. Jane Doe",
-    "email": "jane.doe@example.com",
-    "office": "Room 201"
-  }
-}

data/faq.csv

@@ -1,5 +0,0 @@
-question,answer
-What is the return policy?,You can return any item within 30 days of purchase with a receipt.
-How do I track my order?,Use the tracking link sent to your email after shipping.
-What payment methods are accepted?,We accept credit cards, debit cards, and PayPal.
-How can I contact support?,You can email support@example.com or call 1-800-123-4567.

data/faq1.md

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-# Course Overview
-
-This course covers the fundamentals of machine learning, including supervised and unsupervised learning, neural networks, and reinforcement learning. The course is divided into 12 lectures, each lasting 90 minutes.
-
-## Assignment 1
-
-The first assignment is due on **August 31, 2026**. It requires you to implement a simple linear regression model and evaluate its performance.
-
-## Lecture Schedule
-
-| Lecture | Topic |
-|---------|-------|
-| 1 | Introduction to ML |
-| 2 | Data Preprocessing |
-| 3 | Linear Regression |
-| 4 | Logistic Regression |
-| 5 | Decision Trees |
-| 6 | Random Forests |
-| 7 | Support Vector Machines |
-| 8 | Neural Networks |
-| 9 | Convolutional Neural Networks |
-| 10 | Recurrent Neural Networks |
-| 11 | Reinforcement Learning |
-| 12 | Project Presentations |

data/faq2.md

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-# Frequently Asked Questions
-
-**Q: How many lectures are there in the course?**  
-A: There are 12 lectures in total.
-
-**Q: What is the deadline for Assignment 2?**  
-A: Assignment 2 is due on **September 15, 2026**.
-
-**Q: Where can I find the lecture slides?**  
-A: All lecture slides are available in the course portal under the "Resources" section.
-
-**Q: Can I submit the assignment late?**  
-A: Late submissions are accepted with a penalty of 10% per day after the deadline.

data/faq3.md

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-# Course Materials
-
-- **Lecture Slides**: PDF files for each lecture.
-- **Reading List**: A list of recommended books and papers.
-- **Code Repository**: GitHub repository with starter code and solutions.
-- **Discussion Forum**: For asking questions and collaborating with peers.
-
-**Q: Where is the code repository hosted?**  
-A: The code repository is hosted on GitHub at https://github.com/example/course-ml.
-
-**Q: How do I clone the repository?**  
-A: Use `git clone https://github.com/example/course-ml.git` in your terminal.