Join 30K+ developers in learning how to responsibly deliver value with ML.
A project-based course on machine learning & MLOps fundamentals with a focus on intuition and application that teaches you how to apply ML in industry. All the lessons below are 100% free but we also offer a highly interactive 4-week course where you'll learn how to master MLOps.
⚡️ For more information on our interactive course offering → https://madewithml.com/
Learn the foundations of ML through intuitive explanations, clean code and visuals.
| 🔢 Foundations | 🔥 Machine Learning | 🤖 Deep Learning |
| Notebooks | Linear Regression | CNNs |
| Python | Logistic Regression | Embeddings |
| NumPy | Neural Network | RNNs |
| Pandas | Data Quality | Transformers |
| PyTorch | Utilities |
📆 More topics coming soon!
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Learn how to apply ML to build a production grade product to deliver value.
- Lessons: https://madewithml.com/courses/mlops/
- Code: GokuMohandas/MLOps
| 📦 Product | 📝 Scripting | ♻️ Reproducibility |
| Objective | Organization | Git |
| Solution | Packaging | Pre-commit |
| Iteration | Documentation | Versioning |
| 🔢 Data | Styling | Docker |
| Labeling | Makefile | 🚀 Production |
| Preprocessing | Logging | Dashboard |
| Exploratory data analysis | 📦 Interfaces | CI/CD workflows |
| Splitting | Command-line | Infrastructure |
| Augmentation | RESTful API | Monitoring |
| 📈 Modeling | ✅ Testing | Pipelines |
| Evaluation | Code | Feature store |
| Experiment tracking | Data | |
| Optimization | Models |
📆 New lessons every month!
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Software engineerslooking to learn ML and become even better software engineers.Data scientistswho want to learn how to responsibly deliver value with ML.College graduateslooking to learn the practical skills they'll need for the industry.Product Managerswho want to develop a technical foundation for ML applications.
Lessons will be released weekly and each one will include:
intuition: high level overview of the concepts that will be covered and how it all fits together.code: simple code examples to illustrate the concept.application: applying the concept to our specific task.extensions: brief look at other tools and techniques that will be useful for difference situations.
hands-on: If you search production ML or MLOps online, you'll find great blog posts and tweets. But in order to really understand these concepts, you need to implement them. Unfortunately, you don’t see a lot of the inner workings of running production ML because of scale, proprietary content & expensive tools. However, Made With ML is free, open and live which makes it a perfect learning opportunity for the community.intuition-first: We will never jump straight to code. In every lesson, we will develop intuition for the concepts and think about it from a product perspective.software engineering: This course isn't just about ML. In fact, it's mostly about clean software engineering! We'll cover important concepts like versioning, testing, logging, etc. that really makes something production-grade product.focused yet holistic: For every concept, we'll not only cover what's most important for our specific task (this is the case study aspect) but we'll also cover related methods (this is the guide aspect) which may prove to be useful in other situations.
- I've deployed large scale ML systems at Apple as well as smaller systems with constraints at startups and want to share the common principles I've learned.
- Connect with me on Twitter and LinkedIn
While this content is for everyone, it's especially targeted towards people who don't have as much opportunity to learn. I believe that creativity and intelligence are randomly distributed while opportunities are siloed. I want to enable more people to create and contribute to innovation.
To cite this content, please use:
@misc{madewithml,
author = {Goku Mohandas},
title = {Made With ML},
howpublished = {\url{https://madewithml.com/}},
year = {2021}
}