Understanding Machine Learning

No, this course does not require any coding. It is designed to be a conceptual and introductory course focusing on understanding the basics of machine learning.

Yes, the course concludes with a discussion about the limits and dangers of machine learning, which includes its ethical implications.

There are many different real-world examples of machine learning. Among them are credit card fraud detection, spam filters, chatbots, disease prediction, customer churn rate prediction, and market basket analysis.

Machine learning is a set of data-based tools for generating insights and making predictions. Many sectors use machine learning to make more informed decisions, including banking, marketing, sales, healthcare, logistics, linguistics, education, insurance, and manufacturing.

Various programming languages are capable of machine learning, including Python, R, Java, JavaScript, C , Shell, and Go. Deciding the "best" machine learning language will depend on the purpose or goal of your project.

The difficulty of learning machine learning depends on your background and depth of study. However, our Understanding Machine Learning course simplifies these concepts, requiring no coding and starting with basic definitions. We aim to make machine learning accessible and achievable for all, regardless of their prior knowledge.