Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World (Rheinwerk Computing)

Jason Hodson

ISBN 10: 1493227580 / ISBN 13: 9781493227587

Editore: Rheinwerk Computing, 2026

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Codice articolo 18405250714

Dati bibliografici

• Titolo: Applied Machine Learning: A Practical Guide to Preparing Data, Selecting Algorithms, and Implementing Machine Learning Models in the Real World (Rheinwerk Computing)
• Editore: Rheinwerk Computing
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 1493227580
• ISBN 13: 9781493227587
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three 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 impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business!

In this book, you’ll learn about:

a. 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.

b. 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, clustering, and ensembling.

c. 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.

d. 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.

Highlights include:

1) Real-world use cases
2) Data exploration
3) Data cleaning
4) Model decision framework
5) Regression algorithms
6) Decision trees
7) Clustering
8) Validation metrics
9) Model iteration
10) Interpretability
11) Implementation
12) Monitoring

Informazioni sull?autore

Jason Hodson has worked in data-centric roles for nearly a decade. He currently works as an HR analytics manager, and he has prior experience in a forecasting role using the full range of applied machine learning. In a previous role, Jason wrote the end-to-end code for an enterprise hiring manager and candidate experience process, collaborating with recruiting leaders to understand and leverage data from a company-wide survey. He’s built large data models and dashboards and taught nontechnical users how to adopt and use them. Jason has been a technical mentor in all his roles, helping others develop their analytics and programming skill set. The common thread across Jason’s career is his ability to be a translator for stakeholders, peers, and junior team members. His learning journey also gives him a unique perspective: Before earning a master’s degree in business analytics, he was entirely self-taught. This has made his approach to teaching more practical, allowing concepts to translate better (and faster) into the business world.