is a practical, intensive guide designed to take learners from foundational concepts to advanced applications in real-world data science. Whether you’re transitioning into a data science career, leading AI integration in your organization, or expanding your applied ML toolkit, this book offers a structured, hands-on learning journey that delivers results in just 12 weeks. Focusing on both breadth and depth, this resource equips readers with critical knowledge and implementation skills across a wide spectrum of applied AI technologies and data-driven solutions. • Build a strong foundation in core machine learning principles, including supervised and unsupervised learning, model evaluation, and pipeline development using real datasets and industry tools. • Apply time-series analysis to real-world forecasting problems, gaining experience with seasonality, trend decomposition, and prediction techniques used in finance, IoT, and operations. • Explore computer vision techniques with practical projects, from image classification and object detection to convolutional neural networks (CNNs) and pre-trained vision models. • Understand how to build and optimize recommender systems, applying collaborative filtering, matrix factorization, and hybrid methods to personalize content across platforms. • Develop project-based skills in applied AI and automation, working with end-to-end data pipelines, cloud-based deployment, and intelligent services across domains such as healthcare, retail, and logistics. • Connect applied skills to long-term developments in artificial general intelligence, gaining insight into how today’s hands-on tools contribute to more autonomous, adaptive, and scalable AI systems. Applied Data Science Foundations is more than just theory—it’s your step-by-step guide to mastering data science in action. With its 12-week framework, you'll gain the confidence and capabilities to build and deploy intelligent systems that solve real-world problems.