Machine Learning With Python Programming : 2023 A Beginners GuideКНИГИ » ПРОГРАММИНГ
Название: Machine Learning With Python Programming : 2023 A Beginners Guide: The Definitive Guide to Mastering Machine Learning in Python and a Problem-Guide Solver to Creating Real-World Intelligent Systems Автор: James Harrison Издательство: Orchid Publishing Год: Oct 27, 2023 Страниц: 431 Язык: английский Формат: pdf, epub Размер: 10.3 MB
Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for.
This comprehensive guide takes you on an exciting journey from the basics of Python programming to the depths of neural networks and Deep Learning. It demystifies the complex world of Machine Learning, making it accessible and understandable, regardless of your background.
James begins by discussing Machine Learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.
Understand Machine Learning algorithms, models, and core Machine Learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of Machine Learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply Machine Learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python Scikit-learn library and other powerful tools And much more!
Who This Book Is For: This book is intended for Python programmers who want to add Machine Learning to their repertoire, either for a specific project or as part of keeping their toolkit relevant. Perhaps a new problem has come up at work that requires Machine Learning. With Machine Learning being covered so much in the news these days, it’s a useful skill to claim on a resume.
This book provides the following for Python programmers:
A description of the basic problems that Machine Learning attacks Several state-of-the-art algorithms The principles of operation for these algorithms Process steps for specifying, designing, and qualifying a Machine Learning system Examples of the processes and algorithms Hackable code
To get through this book easily, your primary background requirements include an understanding of programming or Computer Science and the ability to read and write code. The code examples, libraries, and packages are all Python, so the book will prove most useful to Python programmers. In some cases, the book runs through code for the core of an algorithm to demonstrate the operating principles, but then uses a Python package incorporating the algorithm to apply the algorithm to problems. Seeing code often gives programmers an intuitive grasp of an algorithm in the way that seeing the math does for others. Once the understanding is in place, examples will use developed Python packages with the bells and whistles that are important for efficient use (error checking, handling input and output, developed data structures for the models, defined predictor methods incorporating the trained model, and so on).
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