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Machine Learning with Python for Beginners - A Beginner’s Guide to Understanding and Implementing Machine Learning Using Python - cover
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Machine Learning with Python for Beginners - A Beginner’s Guide to Understanding and Implementing Machine Learning Using Python

Saimon Carrie

Erzähler Saimon Carrie

Verlag: Saimon Carrie

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Beschreibung

Dive into the exciting world of machine learning with "Machine Learning with Python for Beginners: A Practical Guide to Understanding and Implementing Machine Learning Using Python". This book is specifically designed for beginners and aspiring data scientists who want to understand the fundamentals of machine learning and how to apply these techniques using Python, one of the most popular programming languages for data analysis and machine learning. 
The book begins by introducing you to Python and its libraries that are essential for machine learning, such as NumPy, Pandas, Matplotlib, and scikit-learn. You'll learn how to set up your Python environment and gain the programming skills necessary to build your first machine learning models. 
Beyond the basics, the book explores more advanced topics such as neural networks and deep learning with TensorFlow and Keras. You’ll learn how these powerful frameworks can be used to develop sophisticated models that are capable of recognizing patterns and making predictions with greater accuracy. 
This book also discusses the ethical considerations and challenges in machine learning, preparing you to handle data responsibly and make informed decisions in your projects. 
"Machine Learning with Python for Beginners: A Practical Guide to Understanding and Implementing Machine Learning Using Python" provides you with the tools and knowledge needed to start building your own machine learning models. Packed with practical examples, tips, and code snippets, this book is an essential guide for anyone looking to harness the power of machine learning using Python. 
 
Dauer: etwa 3 Stunden (02:58:03)
Veröffentlichungsdatum: 23.06.2024; Unabridged; Copyright Year: — Copyright Statment: —