You're already a smart person, you don't need a 1000+ page book to get you started on the web's fastest growing programming platform. Instead, Learn Python in One Hour delivers on the promise of code literacy while saving your most precious commodity - time itself. Volkman's innovative programming-by-example approach means you focus on usage, not mindless detail. Based on the author's sold-out live seminars, you'll see Python's flexible coding technique in action as we refactor from script to procedural to object-oriented during actual problem solving.
In a seven-lesson progression, you'll be exposed to this and more:
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. From the earliest version 15 years ago to the current one, it has constantly evolved with productivity and code readability in mind.
Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is not so straightforward. This book will show you how to do just that: it will show you how Python development should be done. Python expert Tarek Ziadé takes you on a practical tour of Python application development, beginning with setting up the best development environment, and along the way looking at agile methodologies in Python, and applying proven object-oriented principles to your design.
This book is an authoritative exploration of Python best practices and applications of agile methodologies to Python, illustrated with practical, real-world examples.
This book is for Python developers who are already building applications, but want to build better ones by applying best practices and new development techniques to their projects.
The reader is expected to have a sound background in Python programming.
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.
Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.
Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.
Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.
Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have learnt critical aspects of machine learning Python projects and experienced the power of ML-based systems by actually working on them.
This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.
This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.