Building Machine Learning Systems with Python
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One Minute Bottom Line
|If you want to learn ML practical implementations, then this is the "Right" book to refer.|
Machine learning is an intricate philosophy and it involves lot of mathematical complexities to bring it into the practice of data analysis. This book simply eradicates those intricacies of programming and implementation of machine learning algorithms. In all, it makes machine learning code pretty simple. Understanding "WHAT" is machine learning is not the purpose of this book. However, this book is designed around the concept of "HOW" to implement machine learning algorithms. I would like to add here that it is not enough to explain to you "HOW" to program the algorithms, but it also helps you to think "HOW BEST" we can program it. Let me start with some + and a few - of the books. But before that, remember, as the title clarifies, this book is all around (hovers around) Python implementation of machine learning i.e. SCIKIT-LEARN libraries, Scipy and NUMPy. That's the boundary.
- Very clear and precise declaration from Author that this book is more about implementation of ML than Concept.
- It starts with teaching very basic of data analysis of preprocessing and cleaning up the data along with implementation of Array, indexes, Vector and Matrices using python libraries. This helps reader to make aware about WHAT basics they should build before getting into more complex problems of machine learning. I really liked the "tiny" machine learning program. It's like writing "Hello Word" in any other programming book.
- Beauty is that it takes you slowly into the implementation of classification problem, Text data processing, Clustering, Regression and sentiment analysis.
- Though the breadth of topics is vast but it touches every small corner of related topic. For example: When explaining text comparison method it explains how STOP WORD can be done? how to implement TF-IDF for meaningful text comparison? etc.
- I had big time difficulties in understanding correlation and regression. but explanations of supported SCIKIT libraries made it pretty simple.
- I really enjoyed the chapter for implementation of text (post) data comparison and clustering.
- Big data analysis using JUG came as surprise to me when i was about to complete the reading. It is really interesting to compare this topic with Map reduce implementation. My work is still in progress on this...
- overall this book covers almost everything that PYTHON can cover for you in data analysis and machine learning.
- You should know ML concepts in advance. This is not the book to start ML learning. Obviously, It's already proclaimed that it is programming ML in Python.
- Don't expect in-detail explanation of any algorithm. Like, when it says "TF-IDF" it just explains in a paragraph what TF-IDF is, and its implementation in Scikit-learn libraries.
- You mush have a moderate level of understanding of Python. If you are not at all familiar with PYTHON then spend some time on Python primitive data types and programmability before you start this book.
- ..... yeah that's it. I don't have any more points to mention as negative.
Overall, this is a very good book when you have some knowledge on ML (and its algorithms) and Python. But if you don't know how to implement these concepts in data analysis, then this is the book. Go get it!
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