Machine Learning Pocket Reference: Working with Structured Data in Python

Machine Learning Pocket Reference: Working with Structured Data in Python
PDF, ePUB
  • eBook:
    Machine Learning Pocket Reference: Working with Structured Data in Python
  • Author:
    Matt Harrison
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 17, 2019
  • ISBN:
    1492047546
  • ISBN-13:
    9781492047544
  • Language:
    English
  • Pages:
    320 pages
  • Format:
    PDF, ePUB

Book Description
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.
This pocket reference includes sections that cover:
  • Classification, using the Titanic dataset
  • Cleaning data and dealing with missing data
  • Exploratory data analysis
  • Common preprocessing steps using sample data
  • Selecting features useful to the model
  • Model selection
  • Metrics and classification evaluation
  • Regression examples using k-nearest neighbor, decision trees, boosting, and more
  • Metrics for regression evaluation
  • Clustering
  • Dimensionality reduction
  • Scikit-learn pipelines

Content

Chapter 1: Introduction
Chapter 2: Overview of the Machine Learning Process
Chapter 3: Classification Walkthrough: Titanic Dataset
Chapter 4: Missing Data
Chapter 5: Cleaning Data
Chapter 6: Exploring
Chapter 7: Preprocess Data
Chapter 8: Feature Selection
Chapter 9: Imbalanced Classes
Chapter 10: Classification
Chapter 11: Model Selection
Chapter 12: Metrics and Classification Evaluation
Chapter 13: Explaining Models
Chapter 14: Regression
Chapter 15: Metrics and Regression Evaluation
Chapter 16: Explaining Regression Models
Chapter 17: Dimensionality Reduction
Chapter 18: Clustering
Chapter 19: Pipelines

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