Machine Learning Bookcamp

Machine Learning Bookcamp by Alexey Grigorev

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

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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