Foundations of Data Science and Machine Learning

Ready to turn raw data into meaningful insights but don't know where to start? This course provides a clear path into the world of data science, teaching you the fundamental principles and practical skills needed to work with data effectively. By the end of this course, you will understand the complete data science workflow. You'll be able to take a dataset, clean and prepare it for analysis, build a functional machine learning model to make predictions, and evaluate its performance—all through clear, text-based explanations and hands-on exercises. What you'll learn: - Understand the core concepts of data science, machine learning, and their real-world applications. - Practice data cleaning, manipulation, and exploration techniques using the pandas library in Python. - Build your first predictive models for regression and classification tasks with scikit-learn. - Learn to evaluate model performance using standard metrics to understand its effectiveness. - Apply fundamental feature engineering techniques to improve your model's predictive power. - Grasp the basics of prompt engineering to leverage large language models for data analysis tasks. This course begins with essential terminology and foundational concepts before moving into practical skills. You'll progress from understanding data types to building and testing your own machine learning models through a series of written lessons and coding exercises. This course is designed for absolute beginners. No prior experience in data science, machine learning, or statistics is required. A basic familiarity with Python programming is helpful but not necessary to get started. Begin your journey into the world of data today.