alexeygrigorev / mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

Machine Learning Bookcamp

The code from the Machine Learning Bookcamp book

Useful links:

Machine Learning Zoomcamp

Machine Learning Zoomcamp is a course based on the book

  • It's online and free
  • You can join at any moment
  • More information in the course-zoomcamp folder

Reading Plan


Chapter 1: Introduction to Machine Learning

  • Understanding machine learning and the problems it can solve
  • CRISP-DM: Organizing a successful machine learning project
  • Training and selecting machine learning models
  • Performing model validation

No code

Chapter 2: Machine Learning for Regression

  • Creating a car-price prediction project with a linear regression model
  • Doing an initial exploratory data analysis with Jupyter notebooks
  • Setting up a validation framework
  • Implementing the linear regression model from scratch
  • Performing simple feature engineering for the model
  • Keeping the model under control with regularization
  • Using the model to predict car prices

Code: chapter-02-car-price/02-carprice.ipynb

Chapter 3: Machine Learning for Classification

  • Predicting customers who will churn with logistic regression
  • Doing exploratory data analysis for identifying important features
  • Encoding categorical variables to use them in machine learning models
  • Using logistic regression for classification

Code: chapter-03-churn-prediction/03-churn.ipynb

Chapter 4: Evaluation Metrics for Classification

  • Accuracy as a way of evaluating binary classification models and its limitations
  • Determining where our model makes mistakes using a confusion table
  • Deriving other metrics like precision and recall from the confusion table
  • Using ROC and AUC to further understand the performance of a binary classification model
  • Cross-validating a model to make sure it behaves optimally
  • Tuning the parameters of a model to achieve the best predictive performance

Code: chapter-03-churn-prediction/04-metrics.ipynb

Chapter 5: Deploying Machine Learning Models

  • Saving models with Pickle
  • Serving models with Flask
  • Managing dependencies with Pipenv
  • Making the service self-contained with Docker
  • Deploying it to the cloud using AWS Elastic Beanstalk

Code: chapter-05-deployment

Chapter 6: Decision Trees and Ensemble Learning

  • Predicting the risk of default with tree-based models
  • Decision trees and the decision tree learning algorithm
  • Random forest: putting multiple trees together into one model
  • Gradient boosting as an alternative way of combining decision trees

Code: chapter-06-trees/06-trees.ipynb

Chapter 7: Neural Networks and Deep Learning

  • Convolutional neural networks for image classification
  • TensorFlow and Keras — frameworks for building neural networks
  • Using pre-trained neural networks
  • Internals of a convolutional neural network
  • Training a model with transfer learning
  • Data augmentations — the process of generating more training data

Code: chapter-07-neural-nets/07-neural-nets-train.ipynb

Chapter 8: Serverless Deep Learning

  • Serving models with TensorFlow-Lite — a light-weight environment for applying TensorFlow models
  • Deploying deep learning models with AWS Lambda
  • Exposing the Lambda function as a web service via API Gateway

Code: chapter-08-serverless

Chapter 9: Kubernetes and Kubeflow


  • Understanding different methods of deploying and serving models in the cloud.
  • Serving Keras and TensorFlow models with TensorFlow-Serving
  • Deploying TensorFlow-Serving to Kubernetes

Code: chapter-09-kubernetes


  • Using Kubeflow and KFServing for simplifying the deployment process

Code: chapter-09-kubeflow

Articles from


Appendix A: Setting up the Environment

  • Installing Anaconda, a Python distribution that includes most of the scientific libraries we need
  • Running a Jupyter Notebook service from a remote machine
  • Installing and configuring the Kaggle command line interface tool for accessing datasets from Kaggle
  • Creating an EC2 machine on AWS using the web interface and the command-line interface

Code: no code

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Appendix B: Introduction to Python

  • Basic python syntax: variables and control-flow structures
  • Collections: lists, tuples, sets, and dictionaries
  • List comprehensions: a concise way of operating on collections
  • Reusability: functions, classes and importing code
  • Package management: using pip for installing libraries
  • Running python scripts

Code: appendix-b-python.ipynb

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Appendix C: Introduction to NumPy and Linear Algebra

  • One-dimensional and two-dimensional NumPy arrays
  • Generating NumPy arrays randomly
  • Operations with NumPy arrays: element-wise operations, summarizing operations, sorting and filtering
  • Multiplication in linear algebra: vector-vector, matrix-vector and matrix-matrix multiplications
  • Finding the inverse of a matrix and solving the normal equation

Code: appendix-c-numpy.ipynb

Articles from

Appendix C: Introduction to Pandas

  • The main data structures in Pandas: DataFrame and Series
  • Accessing rows and columns of a DataFrame
  • Element-wise and summarizing operations
  • Working with missing values
  • Sorting and grouping

Code: appendix-d-pandas.ipynb

Appendix D: AWS SageMaker

  • Increasing the GPU quota limits
  • Renting a Jupyter notebook with GPU in AWS SageMaker


The code from the Machine Learning Bookcamp book and a free course based on the book


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