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Machine Learning Pocket Reference: Working with Structured Data in Python

Name: Machine Learning Pocket Reference: Working with Structured Data in Python
Price: 18.89 USD
Availability: InStock
Rating: 4.5 (185 reviews)

4.50 of 5 Stars: Databases & Big Data - Machine Learning Pocket Reference: Working with Structured Data in Python

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Description

Book Synopsis: 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 Read more

Details

Looking to master machine learning? Look no further! The Machine Learning Pocket Reference: Working with Structured Data in Python is the ultimate guide you need. Packed with detailed notes, tables, and practical examples, this handy reference is guaranteed to help you navigate the complex world of structured machine learning. Whether you're a programmer, data scientist, or AI engineer, this book is your perfect companion.

Author Matt Harrison has expertly crafted this guide to provide you with the additional support you need during training and as a convenient resource for all your machine learning projects. Learn about classification with structured data, explore methods for predicting continuous values, reduce dimensionality, and much more!

Not only does this pocket reference cover the essentials of machine learning, but it also includes valuable sections on data cleaning, exploratory data analysis, preprocessing steps, feature selection, and model evaluation. Everything you need to excel in your machine learning journey is right at your fingertips!

In addition, the Machine Learning Pocket Reference features examples using popular algorithms like k-nearest neighbor, decision trees, boosting, and more. Discover the power of scikit-learn pipelines and gain insights into clustering and dimensionality reduction techniques.

Don't miss out on this invaluable resource. Take your machine learning skills to the next level and unlock countless opportunities in the world of data science. Get your copy of the Machine Learning Pocket Reference now!

Ready to supercharge your machine learning expertise? Get your copy today!

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