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A review by philipdeherdt
Introduction to Machine Learning with Python: A Guide for Data Scientists by Müller Andreas C., Sarah Guido
5.0
Great way to get started with Python and ML:
- Gives overview of tools/libraries you'll likely need
- Broad overview of algorithms, with a good explanation on how they work and insight into how the main parameters influence behavior (with examples in the book and code to demonstrate how to use. Code is also available on Github.)
- Many guidances as when to use what (depending on which kind of data/problem you've got; which techniques work better + it gives an idea on which techniques are typically used, and which only in specific cases + informs on trade-offs between model accuracy and model complexity)
The chapter on text data is fairly short (more like a managers overview). However, at this stage of learning, it's sufficient for me. It does provide references to other books when you want to go into specific details.
- Gives overview of tools/libraries you'll likely need
- Broad overview of algorithms, with a good explanation on how they work and insight into how the main parameters influence behavior (with examples in the book and code to demonstrate how to use. Code is also available on Github.)
- Many guidances as when to use what (depending on which kind of data/problem you've got; which techniques work better + it gives an idea on which techniques are typically used, and which only in specific cases + informs on trade-offs between model accuracy and model complexity)
The chapter on text data is fairly short (more like a managers overview). However, at this stage of learning, it's sufficient for me. It does provide references to other books when you want to go into specific details.