Welcome to the AI Workshop Code Repository!
This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
- Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
- Assignments: Carefully designed assignments to reinforce your understanding of each topic.
- Solutions: Solutions to assignments to help guide you through the learning process.
- Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
This repository covers a variety of AI/ML topics, such as:
- Data Preprocessing: Learn to clean and prepare data for ML models.
- Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
- Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
- Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
- Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
- Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Each folder contains a README file with carefully selected resources, including:
- Articles: Introductory and advanced articles to understand the theoretical background.
- Videos: Visual aids to reinforce the concepts.
- Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!