# Best machine learning frameworks

Source: https://freeaicourses.com/tools/ml-frameworks
Last checked: 2026-10-02 (free plans and prices on each maker's own pricing page)

## Short answer

Of the 3 machine learning frameworks we rank, PyTorch is our top pick at 9.3 out of 10. If you are new to this, start with scikit-learn. If you are experienced, try Weights & Biases. PyTorch: Free and open source. Use PyTorch for building, training and debugging neural networks.

## Sections

### PyTorch (9.3 out of 10)

The default deep learning library for research and a lot of production work. If you're going to build neural networks yourself, learn this one.

Building, training and debugging neural networks.

It gives you full control, which means more of your own code to get right.

Free and open source.

### scikit-learn (9.0 out of 10)

The workhorse for classic machine learning on tables of data. Most real business problems need this before they need deep learning.

Regression, classification, clustering and solid baselines on tabular data.

It isn't built for deep learning or GPUs. Reach for PyTorch when you need those.

Free and open source under the BSD 3-Clause licence.

### Weights & Biases (7.9 out of 10)

Logs every training run so you can compare experiments instead of guessing which settings worked. It's useful from your second serious project onwards.

Tracking metrics, comparing runs and sharing results with a team.

Overkill for small course projects. A notebook and a results table will do at first.

Free plan for personal projects, with 5 GB of storage a month.

Pro starts at US$60 a month.

## Best courses

- Neural Networks: Zero to Hero
- Practical Deep Learning for Coders
- Kaggle Intro to Machine Learning

## Questions people ask

### What are the best machine learning frameworks in 2026?

Our top pick is PyTorch (9.3/10), followed by scikit-learn and Weights & Biases. The default deep learning library for research and a lot of production work. If you're going to build neural networks yourself, learn this one.

### Which machine learning frameworks are best for beginners?

Start with scikit-learn. The workhorse for classic machine learning on tables of data. Most real business problems need this before they need deep learning.

### Which machine learning frameworks are best for experts?

PyTorch is our pick for experienced users, with Weights & Biases close behind. The default deep learning library for research and a lot of production work. If you're going to build neural networks yourself, learn this one.

### What's the catch with PyTorch?

It gives you full control, which means more of your own code to get right.

### Are there free courses that teach machine learning frameworks?

Yes. We recommend Neural Networks: Zero to Hero (Andrej Karpathy), Practical Deep Learning for Coders (fast.ai) and Kaggle Intro to Machine Learning (Kaggle). Each one is free and reviewed on FreeAICourses.com.

### Which machine learning frameworks have a free plan?

PyTorch, scikit-learn and Weights & Biases have a free plan or free version.

## Related pages

- [Neural Networks: Zero to Hero](https://freeaicourses.com/courses/neural-networks-zero-to-hero)
- [Practical Deep Learning for Coders](https://freeaicourses.com/courses/practical-deep-learning-for-coders)
- [Kaggle Intro to Machine Learning](https://freeaicourses.com/courses/kaggle-intro-machine-learning)
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