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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course will guide you through the essential skills and concepts you need to become proficient in artificial intelligence engineering. You'll start with a strong foundation in Python programming, diving into core data science tools and techniques before advancing to key mathematical principles that power AI algorithms. As you progress, you'll master machine learning techniques and apply them in real-world projects, building confidence and practical knowledge. The course begins with Python programming basics, including control flow, functions, and working with data structures. You'll then move into data science, where you'll learn to handle data using libraries like NumPy and Pandas, followed by data visualization using Matplotlib and Seaborn. This section will prepare you to clean, manipulate, and analyze large datasets efficiently—key skills for any AI engineer. Next, you'll dive into the mathematics behind machine learning, including linear algebra, calculus, and statistics. These concepts are crucial for understanding the inner workings of AI algorithms and building more sophisticated models. You'll also explore machine learning itself, from basic supervised learning models to more advanced techniques like regression, classification, and k-Nearest Neighbors (k-NN). This course is perfect for anyone looking to launch or enhance their career in AI engineering. It is designed for individuals with basic programming knowledge who want to deepen their understanding of Python, data science, and machine learning. The course is suitable for learners with intermediate experience in Python and programming basics. It is a comprehensive introduction to AI engineering with a hands-on, project-based approach. By the end of the course, you will be able to write Python code for AI tasks, clean and manipulate data with Pandas and NumPy, apply mathematical principles to machine learning models, and implement basic machine learning algorithms like regression, classification, and k-NN.