 Название: Python Machine Learning for Beginners: Unlocking the Power of Data. A Beginner's Guide to Machine Learning with Python
Автор: Daniel Garfield
Издательство: May Reads
Год: 2024
Страниц: 148
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
"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.
Contents:
I. Introduction
A. The Importance of Machine Learning in the Modern World
B. Understanding the Basics of Python Programming
C. Setting Up the Development Environment
II. Foundations of Machine Learning
A. Exploring Data Types and Structures in Python
B. Preparing Data for Machine Learning
C. Feature Engineering and Selection Techniques
D. Evaluating and Visualizing Data
III. Supervised Learning Algorithms
IV. Unsupervised Learning Algorithms
V. Deep Learning with Neural Networks
A. Understanding Neural Networks and Deep Learning Concepts
B. Building and Training Feedforward Neural Networks
C. Convolutional Neural Networks (CNN) for Image Classification
D. Recurrent Neural Networks (RNN) for Sequence Data
E. Transfer Learning and Pre-trained Models
VI. Deploying and Evaluating Machine Learning Models
A. Model Deployment and Integration with Web Applications
B. Model Interpretability and Explainability
C. Monitoring and Maintaining Models in Production
D. Ethical Considerations in Machine Learning
VII. Case Studies and Real-World Applications
VIII. Next Steps in Machine Learning
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Python Machine Learning for Beginners: Unlocking the Power of Data. A Beginner's Guide to Machine Learning with Python 
