Welcome to the MLE Guide, a comprehensive repository designed to help beginners learn and become Machine Learning Engineers. This guide is structured to cover various aspects of machine learning, from the basics to advanced topics and industry applications.
The repository is organized into several subdirectories, each focusing on a specific area of machine learning:
- ML for SWE
- This directory provides an introduction to machine learning concepts and their applications in software engineering.
- Intro to Deep Learning & Neural Networks
- This section covers the fundamentals of deep learning and neural networks, including the basics of neural networks, backpropagation, and optimization techniques.
- Beginner's Guide to Deep Learning
- This directory is designed for those new to deep learning, covering topics such as convolutional neural networks, recurrent neural networks, and transfer learning.
- Deep Learning with PyTorch
- This section focuses on using PyTorch for deep learning tasks, including building and training neural networks.
- Building Advanced Deep Learning & NLP Projects
- This directory covers advanced topics in deep learning and NLP, including attention mechanisms, transformers, and generative models.
- Image Recognition with ML
- This section explores image recognition techniques using machine learning, including convolutional neural networks and object detection.
- NLP with ML
- This directory covers natural language processing techniques using machine learning, including text classification, sentiment analysis, and language models.
- Applied ML: DL for Industry
- This section applies deep learning concepts to real-world industry problems, including computer vision, speech recognition, and recommender systems.
- Applied ML: Industry Case Study
- This directory provides case studies of machine learning applications in various industries, including healthcare, finance, and e-commerce.
- ML Interview Prep
- This section offers resources and practice materials to help prepare for machine learning interviews.
- This directory covers large language models, including their architecture, training, and applications.
- This directory contains a list of recommended books on machine learning and deep learning.
This repository is designed to be a comprehensive resource for machine learning engineers. As machine learning continues to evolve, new topics and areas of focus will emerge. We encourage contributions and suggestions for new directories or topics to be added to the repository.
We welcome contributions to the MLE Guide. If you have expertise in a particular area of machine learning or would like to share your knowledge with others, please feel free to create a pull request or submit an issue with your suggestions.
This repository is licensed under the MIT License. See the LICENSE file for details.
If you have any questions or would like to get involved with the MLE Guide, please reach out in discussions.