Machine Learning with Python

Python Machine Learning and Deep Learning: A Practical Guide is a comprehensive book that teaches readers how to use Python to develop machine learning and deep learning tasks, covering data preprocessing, supervised and unsupervised learning, model evaluation, and deployment. It provides practical skills for building and deploying effective models.

Format: Paperback / softback
Length: 486 pages
Publication date: 02 September 2024
Publisher: De Gruyter

Python is a powerful programming language that can be used to develop machine learning and deep learning tasks. This book provides a comprehensive guide to the fundamentals of machine learning algorithms and techniques, covering a wide range of topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and deployment. By leveraging the power of Python, readers will gain practical skills necessary to build and deploy effective machine learning models, making this book an invaluable resource for anyone interested in exploring the exciting world of artificial intelligence.

The book begins by introducing the basics of machine learning, including the definition of machine learning, its applications, and its limitations. It then covers the various machine learning algorithms, including linear regression, logistic regression, decision trees, and neural networks. Each algorithm is explained in detail, with examples and code snippets to help readers understand how they work.

One of the key strengths of this book is its emphasis on data preprocessing. Data preprocessing is the process of cleaning, transforming, and organizing data before it can be used for machine learning. Poor data preprocessing can lead to inaccurate results and poor model performance. The book provides a step-by-step guide to data preprocessing, including techniques such as feature extraction, feature selection, and normalization.

Supervised learning is a type of machine learning that involves training a model to make predictions based on a set of labeled data. The book covers a wide range of supervised learning algorithms, including linear regression, logistic regression, decision trees, and neural networks. Each algorithm is explained in detail, with examples and code snippets to help readers understand how they work.

Unsupervised learning is a type of machine learning that involves training a model to find patterns in unlabeled data. The book covers a wide range of unsupervised learning algorithms, including clustering, association rule mining, and dimensionality reduction. Each algorithm is explained in detail, with examples and code snippets to help readers understand how they work.

Model evaluation is an important step in machine learning. It involves evaluating the performance of a model on a set of test data. The book covers a wide range of model evaluation techniques, including accuracy, precision, recall, and F1 score. Each technique is explained in detail, with examples and code snippets to help readers understand how they work.

Deployment is the process of deploying a machine learning model to a production environment. The book covers a wide range of deployment techniques, including cloud computing, serverless computing, and edge computing. Each technique is explained in detail, with examples and code snippets to help readers understand how they work.

In addition to the technical aspects of machine learning, the book also covers the ethical and social implications of machine learning. Machine learning algorithms can be used to make decisions that have significant impact on society, and it is important to ensure that these decisions are made ethically and responsibly. The book provides a discussion of ethical issues such as bias, discrimination, and privacy, and offers strategies for addressing these issues.

One of the strengths of this book is its practical approach. The book provides a series of hands-on exercises and projects that allow readers to apply the concepts they have learned to real-world problems. These exercises and projects are designed to be challenging, but they are also designed to be accessible to readers of all skill levels.

Another strength of this book is its community. The book has a large and active community of readers and developers, and the author is actively involved in the community. The community provides a platform for readers to share their experiences, ask questions, and receive feedback.

In conclusion, this book is a comprehensive guide to the fundamentals of machine learning algorithms and techniques. It provides a solid foundation in the fundamentals of machine learning, covers a wide range of topics, and emphasizes practical skills. By leveraging the power of Python, readers will gain the practical skills necessary to build and deploy effective machine learning models, making this book an invaluable resource for anyone interested in exploring the exciting world of artificial intelligence.

Dimension: 240 x 170 (mm)
ISBN-13: 9783110697162