All the @madewithml machine learning fundamentals & MLOps lessons are released!

- 🛠 Project-based
- 💻 Intuition & application (code)
- 🏆 26K+ GitHub ⭐️
- ❤️ 30K+ community
- ✅ 47 lessons, 100% open-source

https://t.co/XIhD3wl1DA

🧵 Thread on details & lesson highlights 👇

Who is this course for?

- 💻 Software engineers / Data scientists looking to learn how to responsibly create ML systems.
- 🎓 College grads looking to learn the practical skills they'll need for the industry.
- 🚀 Product Managers who want to develop a technical foundation.

We start with lessons on the fundamentals of ML through intuitive explanations, clean code and visualizations.

📚 Foundations
- Python (variables, functions, classes, decorators)
- NumPy (numerical analysis)
- Pandas (data analysis)
- PyTorch (operations, gradients)

Then we dive into implementing basic ML algorithms 1⃣ from scratch then 2⃣ in PyTorch. Starting from simple models → complex models.

📈 Modeling
- Linear Regression
- Logistic Regression
- Neural Networks
- Data Quality (⚠️ very important)
- Utilities (for loading and training)

We wrap up the fundamentals by implementing deep learning algorithms in PyTorch.

🤖 Deep Learning
- CNNs
- Embeddings
- RNNs
- Transformers

💡 We motivate the need for specific architectures and additional complexity as we implement each method.

🔥 Putting ML in Production! We're going to publicly develop @madewithml's first ML service. Here is the broad curriculum:

- 📦 Product
- 🔢 Data
- 🤖 Modeling
- 📝 Scripting
- 🛠 API
- 🚀 Production

More details (lessons, task, etc.) here: https://t.co/xmMm9XGK9j

Thread 👇

Questions that this thread will answer:

- What is it?
- Who is this course for?
- What is the format?
- What makes this course unique?
- Why constrain to open source tools?
- What are my qualifications?
- Why is this free?
- What are the

What is it?

Putting ML in Production: a guide and code-driven case study on MLOps. We will be developing and deploying Made With ML's first ML service, from Product → ML → Production, with open source tools.

This ML service will act as a foundation for all future ML features and subsequent iterations. The first feature is tagifai - multilabel classification of tags for a project. We'll discuss the need and utility of this feature in the first lesson.

Who is this course for?

- ML developers looking to become end-to-end ML developers.
- Software engineers looking to learn how to responsibly deploy and monitor ML systems.
- Product managers who want to have a comprehensive understanding of the different stages of ML dev.