Machine Learning Bookcamp

The code from the Machine Learning Bookcamp book

Useful links:

• https://mlbookcamp.com: supplimentary materials
• https://datatalks.club: the place to talk about data (and the book: join the #ml-bookcamp channel to ask questions about the book and report any problems)

Machine Learning Zoomcamp

ML Zoomcamp is a course based on the book

• It's online and free
• The course starts on September, 6
• It's possible to join at any moment

Chapters

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

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

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

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

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

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

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

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

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