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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition Paperback – 20 Sept. 2017

4.4 out of 5 stars 186 ratings

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Product details

  • Paperback : 622 pages
  • ISBN-13 : 978-1787125933
  • ISBN-10 : 9781787125933
  • Product Dimensions : 19.05 x 3.58 x 23.5 cm
  • ASIN : 1787125939
  • Publisher : Packt Publishing; 2nd Revised edition (20 Sept. 2017)
  • Language: : English
  • Customer reviews:
    4.4 out of 5 stars 186 ratings

Product description

Review

"I bought the first version of this book, and now also the second. The new version is very comprehensive. If you are using Python - it's almost a reference. I also like the emphasis on neural networks (and TensorFlow) - which (in my view) is where the Python community is heading.

I am also planning to use this book in my teaching at Oxford University. The data pre-processing sections are also good. I found the sequence flow slightly unusual - but for an expert level audience, it's not a major issue."

--Ajit Jaokar, Data Science for IoT Course Creator and Lead Tutor at the University of Oxford / Principal Data Scientist

About the Author

Sebastian Raschka

Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.


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