Welcome to this project-based course on Logistic 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, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory. 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.
PP
Thank You... Very nice and valuable knowledge provided.
AS
Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful
CB
Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.
AS
Gain more understanding about LR and gradient descent practically.
RS
I really enjoyed this course. Thank you for your valuable teaching.
MM
Well explained all the basic components of gradient descent. Exactly as advertised.
ZR
Very Interesting and useful course. It helped me gain additional values and techniques about logistic regression
MS
Problem was that rhyme could not run for more than the alloted time because I had many errors in between because of which I couldn't complete my whole code in the given time.
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Excellent course but requires prior theoretical knowledge of logistic regression and linear regression. I have a suggestion for the instructor. If possible, can you attach conceptual videos that are already available on Coursera like liner regression lecture by Andrew Ng or any other lecture, then it will be beneficial for students. Overall a good project for starters like me.
Thank you
Very helpful for learning logistic regression without using any libraries. Before taking this project one should have a clear understanding of Logistic Regression, then it will be very helpful
Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.
Well explained all the basic components of gradient descent. Exactly as advertised.
Great tool to practice what i learned in Andrew Yng's ML course about Log. Reg.
I really enjoyed this course. Thank you for your valuable teaching.
Thank You... Very nice and valuable knowledge provided.
Able to follow project. Thanks for guiding
Clear explanation and good content. Thanks
good project got to learn a lot of things
Great course ! very informative
Thanks :)
It is one of the best guided project.
Please, keep doing good job
good course a lot to learn
Excelente aprovechamiento
it was an nice experience
Amazing. Must do this
Great project!
nice overview
well balanced