Python: Deeper Insights into Machine Learning - Softcover

Raschka, Sebastian; Julian, David; Hearty, John

Softcover

ISBN 10: 1787128571 ISBN 13: 9781787128576

Verlag: Packt Publishing, 2017

Inhaltsangabe

Leverage benefits of machine learning techniques using Python.

Key Features

• Improve and optimise machine learning systems using effective strategies.
• Develop a strategy to deal with a large amount of data.
• Use of Python code for implementing a range of machine learning algorithms and techniques.

Book Description

Machine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.

The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.

The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it’s time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.

At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.

What you will learn

• Learn to write clean and elegant Python code that will optimize the strength of your algorithms
• Uncover hidden patterns and structures in data with clustering
• Improve accuracy and consistency of results using powerful feature engineering techniques
• Gain practical and theoretical understanding of cutting-edge deep learning algorithms
• Solve unique tasks by building models
• Get grips on the machine learning design process

Who this book is for

This title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.

Über diesen Titel

Über die Autorin bzw. den Autor

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.Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University.Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2017
• Sprache: Englisch
• ISBN 10: 1787128571
• ISBN 13: 9781787128576
• Einband: Taschenbuch
• Anzahl der Seiten: 916
• Kontakt zum Hersteller: preigu GmbH & Co. KG; mail@preigu.de; ; Lengericher Landstr. 19; Osnabrück; Niedersachsen; 49078; Deutschland