Data Science and Machine Learning using Python Workshop

Welcome to the Data Science and Machine Learning using Python Workshop repository! This repository contains all the course materials, including Jupyter notebooks, datasets, and resources, to help you learn and practice data science and machine learning concepts using Python.

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Course Overview

This workshop is designed to provide a comprehensive introduction to Data Science and Machine Learning using Python. The course is divided into two main sections:

1. Python for Data Science: Covers foundational topics like data manipulation, visualization, and statistical analysis using Python libraries such as NumPy, Pandas, and Matplotlib.
2. Machine Learning using Python: Focuses on machine learning concepts, including data preprocessing, model building, evaluation, and advanced techniques like clustering and recommender systems.

Each topic is accompanied by a hands-on lab in the form of a Jupyter notebook, along with relevant datasets for practice.

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Repository Structure

The repository is organized as follows:

.
├── Machine Learning using Python
│   ├── 18_Data_Preprocessing
│   │   ├── Data Preprocessing_lab2.ipynb
│   │   ├── gender_submission.csv
│   │   ├── test.csv
│   │   └── train.csv
│   ├── 19_Feature_Scaling
│   │   ├── Feature Scaling_lab2.ipynb
│   │   └── Social_Network_Ads.xls
│   ├── ... (other machine learning topics)
│
├── Python for Data Science
│   ├── 01_Read and Write Data to Files
│   │   ├── file_1.txt
│   │   ├── oil_Field_1.xls
│   │   ├── oil_Field.csv
│   │   ├── oil_Field.json
│   │   └── Read and Write Data to Files_lab2.ipynb
│   ├── 02_Data_Pickling
│   │   ├── Data Pickling_lab2.ipynb
│   │   ├── North_Sea_Oil_Field.json
│   │   ├── North_Sea_Oil_Field.pkl
│   │   ├── oil_wells.csv
│   │   └── oil_wells.pkl
│   ├── ... (other data science topics)
│
└── requirements.txt

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Getting Started

To get started with the course materials, follow these steps:

1. Clone the Repository

Clone this repository to your local machine using the following command:

git clone https://github.com/yahya-khalaf/Data-Science-and-Machine-Learning-using-Python-Workshop.git

2. Set Up the Environment

Install the required Python libraries by running:

pip install -r requirements.txt

3. Launch Jupyter Notebook

Start Jupyter Notebook to access the course materials:

jupyter notebook

Navigate to the desired topic folder and open the corresponding .ipynb file to begin.

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Course Topics

Python for Data Science

1. Read and Write Data to Files
2. Data Pickling
3. Data Science with Python
4. Mathematical Computing with Python (NumPy)
5. Vectors and Arrays
6. Matrix Multiplication and Dot Product
7. Matrix Transpose and Inverse Matrix
8. Scientific Computing with Python (SciPy)
9. Data Manipulation with Pandas
10. Pandas Series and Data Frames
11. Statistical Analysis using Pandas
12. Data Cleaning Using Pandas
13. Read and Write to CSV and Excel Files
14. Data Visualization in Python using Matplotlib
15. Line, Bar, and Scatter Plots
16. Histogram Graphs

Machine Learning using Python

1. Data Preprocessing
2. Feature Scaling
3. Handling Missing Data
4. Dummy Variables
5. Handling Imbalanced Data
6. Feature Engineering
7. Backward Elimination
8. Forward Elimination
9. Model Validation
10. Principal Component Analysis (PCA)
11. Linear Regression
12. Regression Metrics (RMSE, R², and AIC)
13. Logistic Regression
14. Classification Metrics (Confusion Matrix, ROC, and AUC)
15. Naive Bayes
16. Decision Trees
17. Ensemble Learning
18. Random Forests
19. Gradient Boost, XGBoost, and LightGBM
20. K-Means Clustering
21. Hierarchical Clustering
22. Gaussian Mixture Models (GMM)
23. Content-Based Recommenders
24. Knowledge-Based Recommenders
25. Collaborative Filter-Based Recommenders

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Requirements

The required Python libraries are listed in requirements.txt. You can install them using:

pip install -r requirements.txt

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Contributing

If you’d like to contribute to this repository (e.g., by fixing issues, adding new topics, or improving documentation), feel free to open a pull request. Your contributions are welcome!

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Acknowledgements

Special thanks to all the contributors and educators who helped create and refine these materials. Your efforts have made this workshop possible!

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Contact

For questions or feedback, feel free to reach out:

• Email: [yahya.khalaf54@gmail.com]
• LinkedIn: [https://www.linkedin.com/in/yahya-khalaf54/]

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Happy learning! 🚀

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