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{ "item_title" : "Applied Machine Learning", "item_author" : [" Jason Hodson "], "item_description" : "Put machine learning theory into practice with this hands-on guideLearn 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 businessIn this book, you'll learn about: a. Data PreparationThe 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 needsFollow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.c. Evaluation and IterationAssess and improve the quality of your modelApply 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 MonitoringYour model is ready to go--now see it in actionLearn how to implement the model to make predictions, monitor its performance, and measure its impact for your business. Highlights include: 1) Real-world use cases2) Data exploration3) Data cleaning4) Model decision framework5) Regression algorithms6) Decision trees7) Clustering8) Validation metrics9) Model iteration 10) Interpretability11) Implementation12) Monitoring", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/49/322/758/1493227580_b.jpg", "price_data" : { "retail_price" : "59.95", "online_price" : "59.95", "our_price" : "59.95", "club_price" : "59.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applied Machine Learning|Jason Hodson

Applied Machine Learning : Using Machine Learning to Solve Business Problems

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Overview

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

Details

  • ISBN-13: 9781493227587
  • ISBN-10: 1493227580
  • Publisher: Rheinwerk Computing
  • Publish Date: March 2026
  • Dimensions: 9.9 x 6.9 x 1 inches
  • Shipping Weight: 1.6 pounds
  • Page Count: 440

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