Machine Learning in Python

Business analytics

ISBN:

978-1-952023-87-3

Machine Learning in Python

From Data Collection to Model Deployment

Mark Keith, PhD

Building a model is only part of machine learning. The real challenge is getting it into production and keeping it reliable. This resource takes students through the full journey in Python, from data collection and preparation with pandas and APIs to multiple linear regression, decision trees, classification, ensemble methods, model evaluation, and feature selection. Students learn the two purposes of modeling, causal explanation and prediction, and then deploy, monitor, and manage their own machine learning pipelines. A final project and technical interview preparation round out the experience, along with modern AI-assisted workflows, so students are ready to build, evaluate, and manage models responsibly in real organizations.

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Foreword: What Is Machine Learning? Chapter 1: Data Mining Project Methodology Chapter 2: Python: DataFrames Chapter 3: Python: Data Wrangling in Pandas Chapter 4: Python: Reading/Writing Chapter 5: Retrieving Data: Web Scraping Chapter 6: Retrieving Data: Inspect Element Chapter 7: Retrieving Data: Web Service APIs Chapter 8: Automated Exploratory Data Analysis: Univariate Chapter 9: Automated Data Cleaning and Preparation Chapter 10: Automated Exploratory Data Analysis: Bivariate Chapter 11: Modeling: Regression Chapter 12: Modeling: Forecasting Chapter 13: Modeling: Classification Chapter 14: Modeling: Clustering Chapter 15: ML: Intro to Pipelines Chapter 16: ML: Feature Selection Chapter 17: ML: Algorithm Selection Chapter 18: Text Analytics: Linguistic Features Chapter 19: Text Analytics: Topic Modeling Chapter 20: Image Classification Chapter 21: Recommendation: Collaborative Filtering Chapter 22: Recommendation: Content Filtering Chapter 23: Deployment in Azure Machine Learning (in progress) Chapter 24: Final Project Supplement: Datasets Supplement: Case Studies

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