Fixed all bugs

ajay-dhangar · ajay-dhangar · commit 65f27aaa6388 · 2026-02-14T13:49:48.000+05:30
diff --git a/docs/cybersecurity/index.mdx b/docs/cybersecurity/index.mdx
@@ -91,7 +91,7 @@ Start from the basics and move upward:
 3. [Network Security Basics](./network-security-basics.mdx)
 4. [Cryptography and Encryption](./cryptography-and-encryption.mdx)
 5. [Ethical Hacking Basics](./ethical-hacking-basics.mdx)
-6. [Cybersecurity Tools](/tutorial/docs/cybersecurity/cybersecurity-tools)
+6. [Cybersecurity Tools](./cybersecurity/cybersecurity-tools)
 7. [Incident Response](./incident-response.mdx)
 8. [Best Practices](./best-practices.mdx)
 
diff --git a/docs/git/quiz.mdx b/docs/git/quiz.mdx
@@ -37,8 +37,8 @@ The goal is to help you **learn Git & GitHub hands-on** while having fun.
 ## Next Steps
 Once you complete this quiz:
 - Explore the [Git Advanced Topics](./advanced)
-- Learn about [GitHub Actions](/tutorial/docs/github/github-actions)
-- Contribute to open source with [GitHub Collaboration](/tutorial/docs/github/collaboration)
+- Learn about [GitHub Actions](../github/github-actions)
+- Contribute to open source with [GitHub Collaboration](../github/collaboration)
 
 :::tip
 Try to get a perfect score to unlock your *Git Pro Badge* (coming soon!)
diff --git a/docs/git/roadmap.mdx b/docs/git/roadmap.mdx
@@ -13,11 +13,11 @@ Welcome to the **Git & GitHub roadmap** — a step-by-step guide to help you **m
 Start with the basics to build a strong foundation.
 
 1. **Introduction to Git & GitHub**  
-   Route: [`/tutorial/docs/git/introduction`](/tutorial/docs/git/introduction)  
+   Route: [`/tutorial/git/introduction`](/tutorial/git/introduction)  
    Learn what version control is, why Git exists, and how GitHub works.
 
 2. **Git Basics**  
-   Route: [`/tutorial/docs/git/basics`](/tutorial/docs/git/basics)  
+   Route: [`/tutorial/git/basics`](/tutorial/git/basics)  
    - Create repositories (`git init`)  
    - Track changes (`git add`, `git commit`)  
    - View commit history (`git log`)  
@@ -32,25 +32,25 @@ Start with the basics to build a strong foundation.
 Expand your skills with branching, remotes, and collaboration.
 
 1. **Branching & Merging**  
-   Route: [`/tutorial/docs/git/branching-merging`](/tutorial/docs/git/branching-merging)  
+   Route: [`/tutorial/git/branching-merging`](/tutorial/git/branching-merging)  
    - Create, switch, and delete branches  
    - Merge branches and resolve conflicts  
    - Visualize branches with `git log --graph`
 
 2. **Working with Remote Repositories**  
-   Route: [`/tutorial/docs/git/remotes`](/tutorial/docs/git/remotes)  
+   Route: [`/tutorial/git/remotes`](/tutorial/git/remotes)  
    - Clone repositories  
    - Push and pull changes  
    - Sync forked repositories  
 
 3. **Undoing Changes & Troubleshooting**  
-   Route: [`/tutorial/docs/git/undo`](/tutorial/docs/git/undo)  
+   Route: [`/tutorial/git/undo`](/tutorial/git/undo)  
    - Undo commits (`git reset`, `git revert`)  
    - Discard changes (`git restore`)  
    - Use stash for temporary work
 
 4. **Collaborating on GitHub**  
-   Route: [`/tutorial/docs/github/collaboration`](/tutorial/docs/github/collaboration)  
+   Route: [`/tutorial/github/collaboration`](/tutorial/github/collaboration)  
    - Issues & discussions  
    - Pull requests & code reviews  
    - Project boards  
@@ -60,32 +60,32 @@ Expand your skills with branching, remotes, and collaboration.
 Master advanced Git concepts, automation, and open-source contributions.
 
 1. **Git Advanced Concepts**  
-   Route: [`/tutorial/docs/git/advanced`](/tutorial/docs/git/advanced)  
+   Route: [`/tutorial/git/advanced`](/tutorial/git/advanced)  
    - Rebasing & cherry-picking  
    - Tagging versions  
    - Git hooks & submodules  
    - Recovering deleted commits (`git reflog`)
 
 2. **GitHub Security & Authentication**  
-   Route: [`/tutorial/docs/github/security`](/tutorial/docs/github/security)  
+   Route: [`/tutorial/github/security`](/tutorial/github/security)  
    - SSH keys, HTTPS, personal access tokens  
    - Two-factor authentication  
    - Managing private vs public repositories
 
 3. **GitHub Actions & Automation**  
-   Route: [`/tutorial/docs/github-actions`](/tutorial/docs/github/github-actions)  
+   Route: [`/tutorial/github-actions`](/tutorial/github/github-actions)  
    - Automate builds, tests, and deployments  
    - YAML workflow basics  
    - GitHub Pages deployment example
 
 4. **Open Source Contribution Guide**  
-   Route: [`/tutorial/docs/github/open-source`](/tutorial/docs/github/open-source)  
+   Route: [`/tutorial/github/open-source`](/tutorial/github/open-source)  
    - Find projects to contribute  
    - Fork → Clone → Branch → Commit → Pull Request workflow  
    - Good first issues and contribution etiquette  
 
 5. **Integrations & Tools**  
-   Route: [`/tutorial/docs/github/integrations`](/tutorial/docs/github/integrations)  
+   Route: [`/tutorial/github/integrations`](/tutorial/github/integrations)  
    - VS Code, GitHub Desktop, Git GUI clients  
    - GitHub CLI and IDE integrations  
 
@@ -100,7 +100,7 @@ After completing the roadmap, practice your skills with real-world projects:
 - Set up **team workflows** with multiple collaborators  
 - Automate **CI/CD pipelines** with GitHub Actions
 
-Routes: [`/tutorial/docs/github/projects`](/tutorial/docs/github/projects)
+Routes: [`/tutorial/github/projects`](/tutorial/github/projects)
 
 ## Additional Resources
 
diff --git a/docs/github/github-actions.mdx b/docs/github/github-actions.mdx
@@ -193,7 +193,7 @@ jobs:
 
 ## Next Up
 
-After mastering GitHub Actions, you can explore **Open Source Contribution** — learn how to contribute effectively to projects and build your developer portfolio. 👉 [Next: Open Source Contribution →](/tutorial/docs/github/open-source)
+After mastering GitHub Actions, you can explore **Open Source Contribution** — learn how to contribute effectively to projects and build your developer portfolio. 👉 [Next: Open Source Contribution →](./open-source)
 
 ## Additional Resources
 
diff --git a/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/feature-selection.mdx b/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/feature-selection.mdx
@@ -5,7 +5,7 @@ description: "Techniques for identifying and keeping only the most relevant feat
 tags: [data-science, feature-selection, machine-learning, statistics, overfitting]
 ---
 
-**Feature Selection** is the process of reducing the number of input variables when developing a predictive model. Unlike [Dimensionality Reduction](/tutorial/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/dimensionality-reduction), which transforms features into a new space, Feature Selection keeps the original features but removes the ones that are redundant or irrelevant.
+**Feature Selection** is the process of reducing the number of input variables when developing a predictive model. Unlike [Dimensionality Reduction](./dimensionality-reduction), which transforms features into a new space, Feature Selection keeps the original features but removes the ones that are redundant or irrelevant.
 
 ## 1. Why Select Features?
 
diff --git a/docs/machine-learning/data-engineering-basics/data-formats/json.mdx b/docs/machine-learning/data-engineering-basics/data-formats/json.mdx
@@ -48,7 +48,7 @@ Most ML frameworks use JSON (or its cousin, YAML) to store **Hyperparameters**.
 
 ### C. API Responses
 
-As discussed in the [APIs section](/tutorial/docs/machine-learning/data-engineering-basics/data-collection/apis), almost every web service returns data in JSON format.
+As discussed in the [APIs section](../data-collection/apis), almost every web service returns data in JSON format.
 
 ## 3. The "Flattening" Problem
 
diff --git a/docs/machine-learning/data-engineering-basics/data-formats/xml.mdx b/docs/machine-learning/data-engineering-basics/data-formats/xml.mdx
@@ -74,7 +74,7 @@ for obj in root.findall('object'):
 
 ## 5. The Challenge: Deep Nesting
 
-Just like [JSON](/tutorial/docs/machine-learning/data-engineering-basics/data-formats/json), XML is hierarchical. To use it in a standard ML model (like a Random Forest), you must **Flatten** the tree into a table.
+Just like [JSON](./json), XML is hierarchical. To use it in a standard ML model (like a Random Forest), you must **Flatten** the tree into a table.
 
 ```mermaid
 graph TD
diff --git a/docs/machine-learning/deep-learning/cnn/strides.mdx b/docs/machine-learning/deep-learning/cnn/strides.mdx
@@ -16,7 +16,7 @@ When the stride is set to **2** (or more), the filter jumps two pixels at a time
 ## 2. The Impact of Striding
 
 ### A. Dimensionality Reduction
-Increasing the stride is an alternative to [Pooling](/tutorial/docs/machine-learning/deep-learning/cnn/pooling). By jumping over pixels, the network effectively "downsamples" the image. For example, a stride of 2 will roughly halve the width and height of the output.
+Increasing the stride is an alternative to [Pooling](./pooling). By jumping over pixels, the network effectively "downsamples" the image. For example, a stride of 2 will roughly halve the width and height of the output.
 
 ### B. Receptive Field
 A larger stride allows the network to cover more area with fewer parameters, but it comes at a cost: **Information Loss**. Because the filter skips pixels, some fine-grained spatial details might be missed.
diff --git a/docs/machine-learning/machine-learning-core/introduction-to-ml.mdx b/docs/machine-learning/machine-learning-core/introduction-to-ml.mdx
@@ -71,7 +71,7 @@ Building a model isn't just writing code; it's a circular process:
 
 1. **Define the Goal:** What are we trying to predict?
 2. **Data Collection:** Gathering raw information.
-3. **Data Preprocessing:** Cleaning and scaling (what you learned in the [Data Engineering module](/tutorial/docs/category/data-engineering-basics)).
+3. **Data Preprocessing:** Cleaning and scaling (what you learned in the [Data Engineering module](/category/data-engineering-basics)).
 4. **Model Training:** Feeding data to the algorithm.
 5. **Evaluation:** Testing the model on data it hasn't seen before.
 6. **Deployment:** Putting the model into a real-world app.
diff --git a/docs/machine-learning/machine-learning-core/reinforcement-learning/deep-q-networks.mdx b/docs/machine-learning/machine-learning-core/reinforcement-learning/deep-q-networks.mdx
@@ -5,7 +5,7 @@ description: "Scaling Reinforcement Learning with Deep Learning using Experience
 tags: [machine-learning, reinforcement-learning, dqn, deep-learning, neural-networks]
 ---
 
-**Deep Q-Networks (DQN)** represent the fusion of Reinforcement Learning and Deep Neural Networks. While standard [Q-Learning](/tutorial/docs/machine-learning/machine-learning-core/reinforcement-learning/q-learning) uses a table to store values, DQN uses a **Neural Network** to approximate the Q-value function.
+**Deep Q-Networks (DQN)** represent the fusion of Reinforcement Learning and Deep Neural Networks. While standard [Q-Learning](./q-learning) uses a table to store values, DQN uses a **Neural Network** to approximate the Q-value function.
 
 This advancement allowed RL agents to handle environments with high-dimensional state spaces, such as raw pixels from a video game screen.
 
diff --git a/docs/machine-learning/machine-learning-core/scikit-learn/data-preparation.mdx b/docs/machine-learning/machine-learning-core/scikit-learn/data-preparation.mdx
@@ -47,7 +47,7 @@ Converts categories into integers (). Use this when the order matters (e.g., Sma
 
 ## 3. Feature Scaling
 
-As discussed in our [Data Engineering module](/tutorial/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/feature-scaling), scaling ensures that features with large ranges (like Salary) don't overpower features with small ranges (like Age).
+As discussed in our [Data Engineering module](../../data-engineering-basics/data-cleaning-and-preprocessing/feature-scaling), scaling ensures that features with large ranges (like Salary) don't overpower features with small ranges (like Age).
 
 ### Standardization (`StandardScaler`)
 
diff --git a/docs/machine-learning/machine-learning-core/supervised-learning/classification/knn.mdx b/docs/machine-learning/machine-learning-core/supervised-learning/classification/knn.mdx
@@ -85,7 +85,7 @@ y_pred = knn.predict(X_test_scaled)
 
 KNN suffers significantly from the "Curse of Dimensionality." As the number of features increases, the "volume" of the space grows so fast that even the "nearest" neighbors become very far away.
 
-**Solution:** Always perform [Dimensionality Reduction (PCA)](/tutorial/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/dimensionality-reduction) or [Feature Selection](/tutorial/docs/machine-learning/data-engineering-basics/data-cleaning-and-preprocessing/feature-selection) before using KNN on high-dimensional data.
+**Solution:** Always perform [Dimensionality Reduction (PCA)](../../../data-engineering-basics/data-cleaning-and-preprocessing/dimensionality-reduction) or [Feature Selection](../../../data-engineering-basics/data-cleaning-and-preprocessing/feature-selection) before using KNN on high-dimensional data.
 
 ## References for More Details
 
diff --git a/docs/machine-learning/machine-learning-core/supervised-learning/regression/elastic-net.mdx b/docs/machine-learning/machine-learning-core/supervised-learning/regression/elastic-net.mdx
@@ -120,4 +120,4 @@ print(f"Best L1 Ratio: {model_cv.l1_ratio_}")
 
 ---
 
-**You've now covered all the primary linear regression models! But what if your goal isn't to predict a number, but to group similar data points together?** Head over to the [Clustering](/tutorial/docs/category/clustering) section to explore techniques like K-Means and DBSCAN!
+**You've now covered all the primary linear regression models! But what if your goal isn't to predict a number, but to group similar data points together?** Head over to the [Clustering](/category/clustering) section to explore techniques like K-Means and DBSCAN!
diff --git a/docs/machine-learning/machine-learning-core/unsupervised-learning/clustering/dbscan.mdx b/docs/machine-learning/machine-learning-core/unsupervised-learning/clustering/dbscan.mdx
@@ -5,7 +5,7 @@ description: "Discovering clusters of arbitrary shapes and identifying outliers
 tags: [machine-learning, unsupervised-learning, clustering, dbscan, outliers]
 ---
 
-**DBSCAN** (Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm that views clusters as high-density regions separated by low-density regions. Unlike [K-Means](/tutorial/docs/machine-learning/machine-learning-core/unsupervised-learning/clustering/kmeans), DBSCAN does not require you to specify the number of clusters in advance and can find clusters of **arbitrary shapes**.
+**DBSCAN** (Density-Based Spatial Clustering of Applications with Noise) is a powerful clustering algorithm that views clusters as high-density regions separated by low-density regions. Unlike [K-Means](./kmeans), DBSCAN does not require you to specify the number of clusters in advance and can find clusters of **arbitrary shapes**.
 
 ## 1. How it Works: Core, Border, and Noise
 
