Python: Deeper Insights into Machine Learning

Sebastian Raschka · David Julian · John Hearty

agt 2016 · Packt Publishing Ltd

À propos de cet e-book

Leverage benefits of machine learning techniques using PythonAbout This BookImprove 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.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.

What You Will LearnLearn to write clean and elegant Python code that will optimize the strength of your algorithmsUncover hidden patterns and structures in data with clusteringImprove accuracy and consistency of results using powerful feature engineering techniquesGain practical and theoretical understanding of cutting-edge deep learning algorithmsSolve unique tasks by building modelsGet grips on the machine learning design processIn Detail

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.

Style and approach

This course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.

À propos de l'auteur

John Hearty is a consultant in digital industries with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made signifiant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually John struck out on his own as a consultant offering comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favourite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfi a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation. He currently lives in the UK.