Book Review: Python Machine Learning Blueprints

Python Machine Learning Blueprints is written by Alexander T Combs and is illustrating how to do some machine learning, data science and advanced web scraping using Python.

It shows some real-world examples using some very useful packages:
Python Requests  - Makes it easy to send HTTP requests
Jupyter Notebook - Useful for visualizing data inspection and creating documents with live code and visualisation.
Pandas - Useful for data analysis
Matplotlib - Based on Matlab,  useful for plotting data.
Seaborn - Useful for visual analysis of data, offering for example violin diagrams.
Statsmodels - Useful for working with models and statistical tests.
Scikit Learn - Useful for visualizations, regression, model selection, clustering etc.

feedback@packtpub.com

The examples are clearly presented, along with both code and diagrams showing how the data was analyzed. Some of them are: Finding underpriced apartments, Analyzing and predicting viral content, Building an image similarity engine and more.

One flaw with the book was the chapter about deep learning. It seems that the author didn't get the numbers right, which made it very hard to follow the calculations.

The expected output isn't specified in the second section/second sentence. The weight function got W and X wrong and W2 was set to 0.2 in the text but 0.4 in the equation. This makes it very hard to follow the example.

I have some other pet projects that take time now, so I won't be able to do serious data mining/machine learning now, but once I have time to explore these topics, I'll buy this book,