Foundations of Data Science and Machine Learning with Python

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This course features Coursera Coach! 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. Embark on a hands-on learning journey through data science and machine learning with Python. In this course, you will gain a deep understanding of core data science concepts and machine learning techniques, while mastering essential Python libraries. You will build the skills necessary to analyze datasets, visualize results, and apply machine learning models to real-world data. The course begins with an introduction to data handling, including installing necessary tools like Anaconda, followed by a Python crash course. You will then explore foundational statistical concepts and their application using Python. Next, we delve into building predictive models, from linear regression to polynomial and multiple regression, and understanding their real-world applications. As you progress, you'll dive into machine learning techniques, such as supervised and unsupervised learning, including decision trees, support vector machines, and ensemble learning methods like XGBoost. Finally, you’ll learn how to build recommender systems, helping you understand the intricacies of collaborative filtering and how to improve your model’s predictions. This course is ideal for individuals eager to break into the world of data science and machine learning, as well as those wishing to enhance their Python skills for professional growth. The course assumes basic familiarity with programming concepts, making it perfect for beginners in the field.

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