"Practical Machine Learning" offers a comprehensive guide for programmers who are new to the field of Machine Learning. This book is crafted to help build a strong foundation for implementing real-world Machine Learning solutions without requiring prior knowledge in Linear Algebra, Probability, or Calculus. It emphasizes practical application, with sufficient theory covered as needed. Readers are also directed to external resources for deeper understanding. The prerequisite for this book is a good programming background, preferably in Python, with Numpy and Scikit-Learn as the primary libraries.
This book is hosted using GitHub Pages and is freely accessible at https://hamza310.github.io/.
Many programmers and developers, lacking a background in Machine Learning, often rely on quick, pre-written solutions from varied sources. This approach can lead to suboptimal and bug-prone solutions when applied to real-world problems. Debugging in Machine Learning requires a solid understanding of the underlying principles. This book aims to bridge that knowledge gap, providing the necessary insights to write effective Machine Learning solutions, including a look under the hood of Scikit-Learn.
"The best way of learning about anything is by doing." This principle is at the heart of this book. It features exercises in the form of 10 Tests designed to deepen your understanding of Machine Learning. These 'Tests' are educational tools rather than assessments. Each test comes in two versions, one with solutions and one without, to encourage self-challenge before consulting the answers. Through these exercises, you'll gain hands-on experience with various Machine Learning applications, including Facial Recognition, Image Compression, Stock Price Prediction, and more, catering to a wide range of backgrounds and skill levels.
While Deep Learning is touched upon, especially in the context of Natural Language Processing, it is not the primary focus of this book. The emphasis is more on foundational Machine Learning techniques and applications.
The author is an experienced freelance Machine Learning, ML Ops, and Data Engineer, providing Artificial Intelligence expertise to companies worldwide.
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