Course curriculum

    1. Welcome to the course

    2. How to use this course

    3. Before we begin...

    1. New machine learning project workflow

    2. Processing data for exploratory analysis

    3. Exploratory data analysis in W&B

    4. Preparing data for model training

    5. Training a baseline model

    6. Lesson summary

    7. Test your learning

    8. Assignment 1

    1. Moving beyond baseline

    2. Reproducing experiments

    3. Organizing code for experimentation

    4. Optimizing your model with Sweeps

    5. Getting insights from experiments

    6. Lesson summary

    7. Test your learning

    8. Assignment 2

    1. Introducing model evaluation

    2. Partitioning data

    3. Avoiding data leakage

    4. Choosing an evaluation metric

    5. Evaluation best practices

    6. Using model registry

    7. Running evaluation

    8. Error analysis

    9. Model diagnostics

    10. Working with the test set

    11. Lesson summary

    12. Test your learning

    13. Assignment 3

    1. Congrats! Here's what's next...

    2. More resources for you

    3. Before you go...

About this course

  • Free
  • 35 lessons
  • 3 hours of video content

Watch Course Trailer

Your goals

Sign up for this free Weights & Biases course to:

  • Accelerate and scale your model development

  • Improve your productivity

  • Ensure reproducibility

  • Iterate and train better models, faster

What you'll learn

  • Best practice machine learning workflows

  • Exploratory data analysis with Tables and Reports in W&B

  • Versioning datasets and models with Artifacts and Model Registry in W&B

  • Tracking and analyzing experiments

  • Automating hyperparameter optimization with Sweeps

  • Model evaluation techniques that ensure reproducibility and enterprise-level governance


Hamel Husain

Machine Learning Engineer

Hamel Husain is currently an entrepreneur in residence at, where he builds tools for data scientists and machine learning engineers. Hamel has previously worked at Airbnb, DataRobot, and GitHub where he built a wide array of machine learning products and infrastructure. Hamel has contributed to data and infrastructure tools in open source such as Metaflow, Kubeflow, Jupyter, and Great Expectations. Hamel was also a consultant for over 10 years, and used data science to improve business outcomes in the restaurant, entertainment, telecommunications, and retail industries.

Thomas Capelle

Machine Learning Engineer

Thomas Capelle is a Machine Learning Engineer at Weights & Biases working on the Growth Team. He’s a contributor to fastai library and a maintainer of wandb/examples repository. His focus is on MLOps, wandb applications in industry and fun deep learning in general. Previously he was using deep learning to solve short term forecasting for solar energy at SteadySun. He has a background in Urban Planning, Combinatorial Optimization, Transportation Economics and Applied Math.

Darek Kłeczek

Machine Learning Engineer

Darek Kłeczek is a Machine Learning Engineer at Weights & Biases, where he leads the W&B education program. Previously, he applied machine learning across supply chain, manufacturing, legal, and commercial use cases. He also worked on operationalizing machine learning at P&G. Darek contributed the first Polish versions of BERT and GPT language models and is a leader in the Polish NLP community. He’s a Kaggle competition winner and 3x Kaggle Master.

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Prove your skills in applying principled workflows to solve real machine learning problems