Applied Machine Learning

Name: Applied Machine Learning
Author: Jason Hodson

by Jason Hodson

Computers

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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Echoes summary

The resonance of Jason Hodson's *Applied Machine Learning* within your curated collection powerfully articulates a deep-seated commitment to the practical, hands-on mastery of computational problem-solving. This book doesn't merely introduce machine learning concepts; it plunges you directly into the actionable implementation of real-world applications, a philosophy that immediately links it to the foundational wisdom found in Wes McKinney's *Python for Data Analysis*. Both texts are united by a shared intellectual lineage, championing clarity, efficiency, and the democratizing power of code to achieve tangible outcomes. Where McKinney lays the essential groundwork for data manipulation and analysis using Python, Hodson's *Applied Machine Learning* builds upon this by demonstrating how to translate that analyzed data into predictive models and demonstrable business impact. This connection highlights a desire not just to understand data, but to actively wield it to solve pressing challenges.

Further amplifying this theme of grounded, inferential reasoning is the strong echo with Peter Bruce and Andrew Bruce's *Practical Statistics for Data Scientists*. This pairing underscores a profound commitment to inductive logic, revealing a desire to not just *apply* algorithms but to understand the principles of reliability and model construction that underpin them. Hodson’s detailed approach to data preparation, model selection, and evaluation directly complements the statistical underpinnings provided by the Bruce's work, creating a synergistic learning path from theoretical rigor to practical application. You’ve instinctively gathered works that build a robust framework for understanding how we extract meaning and predictive power from complex information, moving beyond superficial correlations to establish causal links and reliable forecasts.