Python Machine Learning for Beginners

di Daniel Garfield (Autore)

Daniel Garfield, 2025

"Python Machine Learning for Beginners: Unlocking the Power of Data" is a comprehensive beginner's guide that demystifies the world of machine learning using the Python programming language. Whether you are a student, a professional looking to expand your skillset, or simply curious about the fascinating field of machine learning, this book is your gateway to unlocking the power of data. In this book, you will embark on a journey that takes you from the fundamentals of Python programming to understanding the core principles and techniques of machine learning. Step by step, you will learn how to preprocess and explore data, engineer features, build predictive models, and evaluate their performance. With hands-on examples and practical exercises, you will gain a solid foundation in supervised and unsupervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, clustering, and dimensionality reduction. Delving into the realm of deep learning, you will discover the power of neural networks and their applications in image classification, sequence data analysis, and transfer learning. You will also explore the ethical considerations and challenges associated with deploying machine learning models in real-world scenarios. Throughout the book, the emphasis is on making complex concepts accessible to beginners. With clear explanations, code snippets, and illustrative visualizations, you will develop a deep understanding of machine learning techniques and their practical implementation using Python's popular libraries such as scikit-learn, TensorFlow, and Keras. "Python Machine Learning for Beginners: Unlocking the Power of Data" is your essential companion. By the end of this book, you will be equipped with the knowledge and confidence to harness the power of data and embark on your own machine learning projects.

• Autore: Daniel Garfield
• Durata in (hh:mm:ss): 04:51:25
• Anno edizione: 2025
• Editore: Daniel Garfield
• EAN: 9798347795499

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