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Product details
File Size: 4729 KB
Print Length: 368 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (March 9, 2018)
Publication Date: March 9, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07BC3LFKT
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Book is quite useful, however contains alot of coding errors. I wouldn't recommend if completely new to coding in general, however if you are confident debugging (as the errors are generally small), it is useful. Not mentioned are some of the data files which are located on the authors github account (the url links in the book don't work also)
The book starts by explaining each function with an example that makes sense. But it builds on these pieces as the book continues.The approach is well disciplined and pretty quickly you are building solutions. A mix of theory and code with emphasis on the latter. You won’t learn calculus but you will learn how to do it from the code.Good for those who dropped out of Andrew Ng’s courses and mortals wanting results.Me: 40 years development, MBA, and 5 years data science. Imagining and prediction mostly.
Chris Albon has a broad and deep knowledge of data science. This book is a treasure of hints, explanations, and examples for the use of Python and associated libraries, including Pandas, in machine learning. It is not a text, but it much more than cheat sheets.Chris begins with common operations with arrays, vectors, and matrices -- loading, sorting, examining, analyzing, combining, modifying, transforming. Then continues with procedures used to prepare data -- data handling, normalization, standardization, encoding categories, selecting date ranges, dimensionality reduction, feature selection, and cross-validation. Several chapters review models, including techniques for model selection, hyper-parameter tuning, performance metrics, and discussions of fitting and validation.Each topic is clearly explained and accompanied by a short, self-contained, listing of a Python (Version 3) program as illustration.I highly recommend this book to all of us who need a quick refresher related to a command or procedure.
This is a great resource for quick and insightful tips for your machine learning challenges. As the author states, you do need to have python and machine learning experience to get maximum benefit from this book. I would argue that even those with less Machine Learning experience can learn a lot from the first 8 chapters. Chapters 9-21 are definitely best suited for someone that does have some experience using scikit-learn.Bottom line, this is a useful resource to have next to your computer when those machine learning problems pop up in your day to day work.
I loved this book for helpful code on python. It isn't too detailed because it covers a wide range of topics, however it really sets up a base to help google further. Highly recommend
This book is perfect to have on your desk to look up a quick how-to on just about any data science task. It has everything from data wrangling to advanced machine learning techniques.
This is exactly the book a practitioner who is picking up python is looking for. A cookbook that touches on every step of the model development cycle, with simple to follow examples. Only if it also shows me tips of doing general plots, and drills a bit deeper into pandas.
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