"Introduction to Machine Learning with Python" is a practical guide designed for students, educators, and aspiring data scientists who want to build a strong foundation in machine learning using Python. This book presents complex concepts in a simplified manner, making it accessible even for beginners with basic programming knowledge. Covering essential topics such as data preprocessing, supervised and unsupervised learning, model evaluation, and real-world project applications, the book uses clear explanations and hands-on coding examples with libraries like scikit-learn and pandas. Each chapter builds logically upon the last, helping readers not only understand theory but also apply it through practical experiments. With a strong focus on conceptual clarity and Python-based implementation, this book serves as a bridge between academic learning and real-world machine learning applications. Whether you're a student, instructor, or self-learner, this book will help you confidently step into the world of machine learning with Python.