Applied Machine Learning

In this book, you’ll learn about:

1. Data Preparation

   The first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.

2. Model Selection

   Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, and clustering.

3. Evaluation and Iteration

   Assess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data.

4. Implementation and Monitoring

   Your model is ready to go—now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business.