This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, Handling text or numerical data, model selection, and dimensionality reduction and many other topics.
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.
Content:
Vectors, Matrices and Arrays
Loading Data
Data Wrangling
Handling Numerical Data
Handling Categorical Data
Handling Text
Handling Dates and Times
Handling Images
Dimensionality Reduction Using Feature Extraction
Dimensionality Reduction Using Feature Selection
Model Evaluation
Model Selection
Linear Regression
Trees and Forests
K-Nearest Neighbors
Logistic Regression
Support Vector Machines
Naïve Bayes
Clustering Neural Networks
Saving and Loading Trained Models
| Title |
Machine Learning With Python Cookbook: Practical Solutions From Preprocessing To Deep Learning
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| ISBN-13 |
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| ISBN-10 |
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| Publisher / Imprint |
Shroff
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| Binding |
Paperback
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| Edition |
1st Edition
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| Publication Date |
2026
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| Number of Pages |
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Table of contents is being compiled for this publication.
