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17.8 MB
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A62A2A959722D3D1C5C7BAAB6A62E1EC51364AC1
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March 1, 2026, 1:45 p.m.
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(Last updated: March 1, 2026, 1:46 p.m.)
| File | Size |
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| Hodson J. Applied Machine Learning. Using ML to Solve Business Problems 2026.pdf | 17.8 MB |
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| Uploaded by andryold1 | Size 17.8 MB | Health [ 49 /11 ] | Added 2026-03-01 |
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| Uploaded by polara121 | Size 1014.8 KB | Health [ 0 /0 ] | Added 2023-07-01 |
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SOURCE: Hodson J. Applied Machine Learning. Using ML to Solve Business Problems 2026
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COVER
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MEDIAINFO
Textbook in PDF format
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
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