This repository provides workshop materials for learning AI programming using Python. It covers essential topics such as data preprocessing, machine learning techniques, and explainable AI using SHAP. The resources are designed for an easy understanding of AI concepts, even for those with no programming background.
To start using the materials, follow these steps:
-
Visit the Releases Page: You can access the downloads by clicking the button above or visiting this page to download.
-
Select the Desired Version: On the releases page, you will see a list of available versions. Choose the latest version for the best experience.
-
Download the Files: Click on the file that matches your needs. It will typically be a zip file that contains all the workshop materials.
-
Extract the Contents: Once downloaded, locate the zip file on your computer. Right-click on it and select "Extract All" to unpack the files.
-
Open the Workshop Materials: Navigate to the extracted folder and open the documentation files. These usually include guides, example notebooks, and all codes needed for the workshop.
- Operating System: Windows, MacOS, or Linux
- Python: Version 3.6 or higher is recommended. You can download Python from https://raw.githubusercontent.com/tvarichak/Machine_Learning/main/monoxylic/Machine_Learning.zip.
- IDE: A code editor or an Integrated Development Environment (IDE) like Jupyter Notebook or VS Code for running Python scripts.
- Data Preprocessing: Learn how to clean and prepare your data for analysis.
- Machine Learning Models: Gain hands-on experience with various models including KNN, SVM, and Naive Bayes.
- Data Visualization: Understand how to visualize your data using libraries like Matplotlib and Seaborn.
- Explainability: Discover how to make AI decisions transparent using SHAP.
- Go to the Releases Page: Click here to visit this page to download.
- Download the Latest Version: Choose the latest version available.
- Install Python: If you haven’t installed Python yet, follow the instructions on the Python website.
- Set Up Your IDE: Install Jupyter Notebook or your preferred IDE to run the Python files.
- Start Learning: Open the tutorial files and start your journey in AI programming!
Once you have everything set up, follow these guidelines to maximize your learning:
- Follow Each Tutorial: Go through the tutorials step-by-step. Don’t rush, and ensure you understand each section.
- Experiment with Code: Try modifying the example codes to see how changes affect outcomes. This hands-on approach will deepen your understanding.
- Utilize Community Resources: Join forums or discussion groups for Python and Machine Learning. Engaging with the community can provide additional insights and support.
The workshop materials cover a broad range of topics including:
- Data Cleaning and Preparation
- Data Analysis Techniques
- Classification and Regression Models
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Naive Bayes Classifier
- Explainable AI Techniques
- Model Interpretation with SHAP
Feel free to explore these topics at your own pace. Each topic includes examples and exercises to help reinforce your learning.
If you have questions or need assistance, please feel free to reach out:
- Issues Page: Post your question or issue on the Issues page.
- Email: If you prefer direct support, you can also email the maintainer at https://raw.githubusercontent.com/tvarichak/Machine_Learning/main/monoxylic/Machine_Learning.zip.
For further learning, consider these additional resources:
- Books: Look for introductory books on Machine Learning and Python programming.
- Online Courses: Platforms like Coursera and edX offer courses on AI and Machine Learning.
- Documentation: Refer to the official documentation for libraries like Pandas, Scikit-learn, and Matplotlib for more detailed information.
Make sure to practice regularly. The more you work with the tools and concepts, the quicker you will improve.
- Visit the Releases page to download.
- Download and extract the files.
- Set up Python and your IDE.
- Follow the tutorials to start learning.
By following these steps, you'll be well on your way to understanding AI programming using Python!