Linear Regression with NumPy and Python
22.324 ya inscrito
22.324 ya inscrito
Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por AD24 de may. de 2020
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.
por NB17 de may. de 2020
Great course , concepts were also explained nicely but somewhere I felt lost and was like what's going on . But rather than that everything was great and great experiencing ML while viewing it.
por AV12 de jun. de 2020
It was nice to know how to implement the knowledge I have already gathered. Some prior experience of basic level surely required to understand effectively. Overall worth mine time.
por AA1 de nov. de 2020
Really Good Content, I learnt more however the instructor didn't explain the mathematical expressions of Gradient Descent and matplotlib in details.