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.
Status: Exploratory Data Analysis
Exploratory Data Analysis
Status: Python Programming
Python Programming
Beginner·Guided Project·2 hours
Featured reviews
5.0
·Reviewed Aug 29, 2020
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
4.0
·Reviewed Jul 14, 2020
Gain more understanding about LR and gradient descent practically.
5.0
·Reviewed Nov 7, 2021
Well explained all the basic components of gradient descent. Exactly as advertised.
4.0
·Reviewed May 31, 2020
Very Interesting and useful course. It helped me gain additional values and techniques about logistic regression
5.0
·Reviewed May 23, 2020
Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.
5.0
·Reviewed Jun 8, 2020
I really enjoyed this course. Thank you for your valuable teaching.
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S
Sambhaw
5.0
·Reviewed Aug 2, 2020
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
A
Arnab
5.0
·Reviewed Aug 30, 2020
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
C
CHINMAY
5.0
·Reviewed May 24, 2020
Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.
M
MV
5.0
·Reviewed Nov 8, 2021
Well explained all the basic components of gradient descent. Exactly as advertised.
J
Juan
5.0
·Reviewed Jun 7, 2020
Great tool to practice what i learned in Andrew Yng's ML course about Log. Reg.
R
ramya
5.0
·Reviewed Jun 9, 2020
I really enjoyed this course. Thank you for your valuable teaching.
P
Punam
5.0
·Reviewed Apr 4, 2020
Thank You... Very nice and valuable knowledge provided.
