More programming background was necessary

because of cloud desktop, speed of videos was very slow

I am really happy with this course. I was needing to learn how to build a simple linear regression model using only Python and NumPy, and after finding countless articles and other tutorials that made little sense (I'm a complete noob) I finally found this one. I was skeptical because of my luck with the other resources I found, but this completely did the trick. Being able to code along with the video was so helpful, too. I'm so happy I finally found this tutorial that guided me step by step with clear explanations.

Understand how linear regression works behind the scenes! This project was very valuable for me because it helped me to turn my theoretical knowledge into practice. Enrol this project only if you're familiar with Python, NumPy, pandas, matplotlib, seaborn, matrix algebra, linear regression, gradient descent, Jupyter Notebook. Otherwise, you will not understand anything!

I have read many articles and enrolled in several courses attempting to teach linear regression from scratch. This course provides the best balance of sufficient math to enable a deeper understanding and the practicality of seeing a simple implementation of the algorithm actually working in numpy. Hope to see a similar project for neural networks.

This project was just the right one to get me started on my path to machine learning. I am currently setting out to explore Machine Learning and was in a dire need of learning some basics. I would like to thank Coursera and the project instructor to guide me to learn some new and valuable skills.

This refreshed my foundations of machine learning skills with absolutely simple libraries that are rather powerful when comes in predictions and the guide was really helpful throughout the course and beginners can get clear idea of what is happening with models.

Can definitely call this course an informative one as the instructor literally explained each and every line of the code. But it'd be much beneficial if the candidate gather a basic understanding of Linear regression before starting this course

It was helpful as recently I had seen a linear regression problem which was too complicated. But this project helped me understand the basics properly to continue my interest in Python language. It was interesting. Thank You

Good Course . I really wanted someone to guide me write the library functions from scratch to help me understand the core mathematical concept behind the linear regression. This course was what I wanted all along.

This course covered all the concept taught in the machine learning course of Coursera. I am glad that Snehan Sir was so clear during his guide lecture that I was able to relate my concepts with the project work.

It is a great project and an excellent experience to learn practical exposure to Linear regression with nmpy and python. I am waiting to get another project.

Best Project ever we have seen, all plotting and code are explain in very well manner and its definitely increase my knowledge in machine learning

Very interesting and useful information. The platform takes a long time to start, but the content is valuable.

This was cool and i loved it. looking forward for more such opportunities. Thank you for this course

very useful this course for begineer . i got it lots of knowledge about numpy and python.

Thank you

The course was awesome and a broad discussion about linear regression with NumPy and Python

thank you, need little patience to understand cost function, prediction, regression..etc.

I really enjoyed working with this project. Thank you so much for the valuable teaching.

Instructor has good delivery style .Good content .Satisfied with this project Thank u.

thank you, very helpful project! I will continue exploring this interesting field! :)

It's a bit difficult at the beginning but I think it's a good way for learning fast

Very good project for beginner. Instructor completed the project without library.

Excellent course. Clear explanations and very concise yet complete examples.

i like this, make me understanding Liniear Regresion with bumpy & Pyhton