Machine learning has become an integral part of many commercial applicationsand research projects, but this field is not exclusive to large companies withextensive research teams. If you use Python, even as a beginner, this bookwill teach you practical ways to build your own machine learning solutions.With all the data available today, machine learning applications are limitedonly by your imagination.
You'll learn the steps necessary to create a successful machine-learningapplication with Python and the scikit-learn library. Authors Andreas Mullerand Sarah Guido focus on the practical aspects of using machine learningalgorithms, rather than the math behind them. Familiarity with the NumPy andmatplotlib libraries will help you get even more from this book.
With this book, you'll learn:
Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which dataaspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processingtechniques Suggestions for improving your machine learning and data science skills

2024-09-11