Textbook in PDF format This book covers all aspects of machine learning (ML) from concepts and math to ML programming. ML concepts and the math associated with ML are written from an application perspective, rather than from a theoretical perspective. The book presents concepts and algorithms precisely as they are used in real-world applications, ensuring a seamless and practical understanding with no gap between theory and practice. In a distinctive approach, the book's content is complemented by video lectures whose details can be found inside the book. This innovative approach offers readers a multimedia learning experience, accommodating different learning preferences, and reinforcing the material through visual and auditory means. If you are new to Artificial Intelligence and Machine Learning, this could be the first book you read and the first video course you take. About the Author. About the Book. Introduction to AI. Pattern Recognition. Introduction to Machine Learning. Variables and Data Types. Descriptive Statistics. Inferential Statistics. Libraries in Machine Learning. Simple Linear Regression. Multiple Linear Regression. Logistic Regression. Decision Trees, Bagging, and Boosting. Naïve Bayes. Support Vector Machine (SVM). Unsupervised Machine Learning. Deep Learning