Enroll Course: https://www.udemy.com/course/data-science-linear-regression-in-python/
If you’re curious about how groundbreaking AI technologies like ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion operate, then the course ‘Deep Learning Prerequisites: Linear Regression in Python’ on Coursera is your perfect starting point. This course offers a solid foundation in one of the most fundamental machine learning techniques: linear regression. Not only does it cover the theoretical aspects, including derivations and real-world applications, but it also equips you with practical coding skills in Python.
What makes this course particularly valuable is its approach to teaching by doing. Instead of just plugging into libraries, you’ll learn how to build your own linear regression models from scratch, fostering a deeper understanding of the underlying mechanics. The instructor emphasizes visualization and experimentation, aligning with Richard Feynman’s philosophy: “What I cannot create, I do not understand.”
The course begins with simple 1-D linear regression to illustrate concepts like Moore’s Law, then progresses to multi-dimensional regression for more complex predictions, such as estimating blood pressure from age and weight. Additionally, it covers essential machine learning topics like overfitting, train-test splits, and model generalization, making it ideal for aspiring data scientists, programmers, and engineers.
No external materials are needed—just Python and some libraries, all of which are free. Whether you’re a beginner or looking to deepen your understanding of machine learning’s inner workings, this course offers invaluable insights and hands-on coding experience. I highly recommend it for anyone eager to move beyond superficial usage to truly understanding how AI models work behind the scenes.
Enroll Course: https://www.udemy.com/course/data-science-linear-regression-in-python/