Machine Learning Bookcamp

Machine Learning Bookcamp by Alexey Grigorev
Publication: Summer 2021 (expected)

Machine Learning Bookcamp: learn machine learning by doing projects and get the skills needed to work as a data scientist or machine learning engineer.

Table of Contents

  • 1. Introduction to machine learning
  • 2. Machine learning for regression
  • 3. Machine learning for classification
  • 4. Evaluation metrics for classification
  • 5. Deploying machine learning models
  • 6. Decision trees and ensemble learning
  • 7. Neural networks and deep learning
  • 8. Serverless deep learning
  • 9. Kubernetes and Kubeflow
  • Appendix A. Installation
  • Appendix B. Introduction to Python
  • Appendix C. Introduction to NumPy
  • Appendix D. Introduction to Pandas
  • Appendix E. AWS SageMaker

The code for the book is available on Github: mlbookcamp-code.

Author

Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books: Mastering Java for Data Science and TensorFlow Deep Learning Projects.

For updates, follow Alexey on Twitter (@Al_Grigor) and LinkedIn (agrigorev).

Articles

We extracted the core concepts from the book into articles

Courses

There are some courses based on the book. They are now under active development

Talk about the book

If you’d like to talk about the book with others or ask author any question, join DataTalks.Club – it’s a community of people who love data. To talk about the book, join #ml-bookcamp channel.

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Machine Learning Bookcamp. Hosted on GitHub Pages