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A collection of resources around Machine Learning and Artificial Intelligence.

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# Machine Learning Lagos - Resources Repository 📚

Welcome to the Machine Learning Lagos resources repository! This repository is designed to organize and share valuable resources that support learning, collaboration, and project development within our community. Whether you're new to machine learning or an experienced practitioner, you'll find a wealth of materials here to enhance your journey.

# Repository Structure

This repository is organized into several main sections:

## 1. [Study Groups](/study-groups)

This folder contains materials for our various study groups. Each subfolder or markdown file covers a different topic, such as "Introduction to Machine Learning," "Deep Learning," "Natural Language Processing," and more. These resources are curated to provide comprehensive learning paths and references for each topic.

## 2. [General Resources/Tutorials](/general-resources)

Here, you’ll find resources that offer broad insights into AI and machine learning, including applications in different industries, essential tools and frameworks, ethical considerations, and foundational knowledge. These resources are ideal for anyone looking to understand the impact and scope of AI in the real world.

## 3. [Projects](/project-ideas)

The projects section showcases various machine learning and AI projects developed within our community. It includes templates, beginner projects, intermediate projects, and advanced projects. This is a great place to explore real-world applications of machine learning and contribute to ongoing community projects.

## 4. [Google AI](/google-ai)

This folder is dedicated to resources on Google’s AI tools and technologies. It serves as a comprehensive guide for exploring Google AI products to learn, build solutions and accelerate your machine learning projects.

## 5. [Events](/events)

The events section contains information and resources related to past and upcoming events, including workshop recordings, speaker slides, and event summaries. It’s a valuable archive for members who want to revisit or catch up on previous sessions.

# Getting Started

1. Browse the folders to find resources that interest you.
2. Join our study groups, take part in our projects, or view our event recordings.
3. Connect with the community on [Nasio](https://proxy.nas.io/machine-learning-lagos), [Twitter](https://x.com/mlinlagos), and [LinkedIn](https://www.linkedin.com/company/machine-learning-lagos/).

# How to Contribute

We encourage all community members to contribute to this repository! Here’s how:

1. Fork the repository.
2. Create a branch for your contribution.
3. Add your content to the appropriate folder.
4. Submit a pull request with a clear description of your additions.

Your contributions help make this repository a rich learning environment for everyone!

# Contact

For any questions or suggestions, please reach out to the Machine Learning Lagos team via [email](machinelearninglagos@gmail.com) or open an issue in this repository.

Let’s learn, build, and grow together!

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