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.


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).


We extracted the core concepts from the book into articles


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Machine Learning Bookcamp. Hosted on GitHub Pages