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Use Powerful Python Libraries To Implement Machine Learning And Deep Learning

Posted by Technical Literature on

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Machine learning is starting to dominate the software world, and now deep learning is reaching machine learning. Experience and perform at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers useful facts and techniques you need to build and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and improved, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been thoroughly updated to incorporate new improvements and additions to this ingenious machine learning library.

Sebastian Raschka and Vahid Mirjalili's unparalleled insight and expertise introduce you to machine learning and deep learning algorithms from scratch and show you how to apply them to practical industry challenges using realistic and compelling examples. By the end of the book, you will be ready to face the new data analysis opportunities in today's world.

If you have read the first edition of this book, you will be delighted to find a new balance of traditional ideas and fresh insights into machine learning. Every chapter has been critically updated, and there are new chapters on essential technologies. You learn to work with TensorFlow more deeply than ever before and get primary coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

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