├── README.md
├── classification-in-depth-explanation.ipynb
├── decision-trees-in-depth-explanation.ipynb
├── dimensionality-reduction-in-depth-explanation.ipynb
├── ensemble-methods-in-depth-explanation.ipynb
├── regression-in-depth-explanation.ipynb
├── requirements.txt
├── svm-in-depth-explanation.ipynb
└── unsupervised-techniques-in-depth-explanation.ipynb


/README.md:
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 1 | # ML Algorithms In Depth 📚
 2 | 
 3 | Welcome to the ML Algorithms In Depth repository! This repository is dedicated to providing in-depth explanations of various machine learning algorithms. The goal is to help learners understand the concepts and workings of each algorithm step-by-step. 🚀
 4 | 
 5 | ## Topics 📋
 6 | 
 7 | - Classification: In-Depth Explanation
 8 | - Regression: In-Depth Explanation
 9 | - SVM (Support Vector Machines): In-Depth Explanation
10 | - Decision Tree: In-Depth Explanation
11 | - Ensemble Methods: In-Depth Explanation
12 | - Dimensionality Reduction: In-Depth Explanation
13 | - Unsupervised Techniques: In-Depth Explanation
14 | 
15 | Learning these topics will help you gain a better understanding of machine learning algorithms and their applications. 💡
16 | 
17 | ## Viewing Code Blocks 🚧
18 | 
19 | If GitHub can't render the code blocks properly, you can open it in `Incognito Tab`. Alternatively, you can download the  file and open it in your local Jupyter Notebook environment.
20 | 
21 | ## How to Contribute 🤝
22 | 
23 | Contributions to this repository are welcome! If you would like to contribute an in-depth explanation of a machine learning algorithm or improve an existing one, follow these steps:
24 | 
25 | 1. Fork this repository.
26 | 2. Create a new branch for your work.
27 | 3. Add your in-depth explanation to the corresponding topic file.
28 | 4. Commit your changes.
29 | 5. Push to the branch.
30 | 6. Create a pull request.
31 | 
32 | ## Contributors ✨
33 | 
34 | A big thanks to all the contributors who have helped make this repository possible! 🙌
35 | 
36 | - [Aditya Goyal](https://www.kaggle.com/goyaladi)
37 | 
38 | ## Setup 💻
39 | 
40 | To set up the project locally, follow these steps:
41 | 
42 | 1. Clone the repository:
43 |    ```bash
44 |     `https://github.com/I-AdityaGoyal/ML_Algorithms-In_Depth.git`
45 |     ```
46 | 2. Navigate to the project directory: 
47 |    ```bash
48 |    `cd ML_Algorithms-In_Depth`
49 |    ```
50 | 3. Install the required dependencies: 
51 |     ```bash
52 |    `pip install -r requirements.txt`
53 |    ```
54 | 4. You are all set! Explore the in-depth explanations in each topic file.
55 | 
56 | Let's learn and grow together! Feel free to explore the topics and deepen your understanding of various machine learning algorithms. Happy learning! 🌟
57 | 


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/requirements.txt:
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1 | pandas
2 | numpy
3 | sklearn
4 | matplotlib
5 | seaborn
6 | xgboost
7 | 


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