In this blog, we explore a curated list of must-read books for anyone interested in machine learning. These books cover a wide range of topics and cater to different levels of expertise. From Christopher M. Bishop's "Pattern Recognition and Machine Learning," which provides a comprehensive introduction to machine learning, to Aurélien Géron's "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow," focusing on practical implementations, each book offers unique benefits. Ian Goodfellow, Yoshua Bengio, and Aaron Courville's "Deep Learning" book is considered the go-to reference for understanding deep learning algorithms, while Andriy Burkov's "The Hundred-Page Machine Learning Book" serves as a concise yet comprehensive overview for beginners. Lastly, Andrew Ng's "Machine Learning Yearning" imparts practical guidance based on real-world experience. These books, accompanied by their respective links, provide opportunities for readers to enhance their machine learning skills and stay abreast of the latest developments in the field.