Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following compelling use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its business impact over time. With sample code for download, this book gives you everything needed to implement machine learning models that solve real business problems!
- Practical introduction to applied machine learning across three real-world use cases
- Select and implement the right machine learning model for your business problem
- Evaluate model results and monitor your models long term
Data Preparation
The first step is to understand your data. Learn about 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.
Model Selection
Choose the machine learning model that fits your problem! Follow a structured model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, and clustering.
Evaluation and Iteration
Assess and improve the quality of your model! Apply a variety of validation metrics, enhance interpretability to avoid black box code, and iterate through feature engineering and adding or removing data.
Implementation and Monitoring
Your model is ready—now put it to work! Learn how to implement your model to generate predictions, monitor its performance over time, and measure its impact for your business.
Jason Hodson is currently working in a forecasting role that uses the full range of applied machine learning. He’s been working in data-centric roles for nearly a decade. In one of his roles, Jason wrote the end-to-end code for the company’s enterprise hiring manager and candidate experience process while also interfacing with the recruiting leaders to understand and leverage the survey. He’s helped build large data models and dashboards, while also helping nontechnical users to adopt and use them. Jason has been a technical mentor for a number of individuals in his various roles, where he’s helped them develop their analytics and programming skillset. The common thread across Jason’s career is his ability to be a translator for stakeholders, peers, and other junior team members. His learning journey also gives him a unique perspective, as he was self-taught in the analytics space before getting his master’s degree in business analytics. This has made his teaching more practical, allowing concepts to translate better (and faster) into the business world.
