Chevron Left
Back to Logistic Regression with Python and Numpy

Learner Reviews & Feedback for Logistic Regression with Python and Numpy by Coursera Project Network

4.5
stars
137 ratings
23 reviews

About the Course

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....

Top reviews

DP
Apr 8, 2020

Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.

MT
Mar 9, 2020

Easy to follow along, each step was made very clear, and I understood the justification behind steps.

Filter by:

1 - 23 of 23 Reviews for Logistic Regression with Python and Numpy

By shiva s t

Mar 9, 2020

it is a great course and successfully trained my ml model

By Duddela S P

Apr 9, 2020

Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.

By Megan T

Mar 10, 2020

Easy to follow along, each step was made very clear, and I understood the justification behind steps.

By Raj K

Apr 29, 2020

Great learning material and hands-on platform!

By Pranjal M

Jun 14, 2020

A very good project for learners

By Ashwin K

Sep 2, 2020

An amazing Project

By Gangone R

Jul 2, 2020

very useful course

By JONNALA S R

May 7, 2020

Good Initiation

By Nandivada P E

Jun 15, 2020

super course

By Doss D

Jun 23, 2020

Thank you

By Saikat K 1

Sep 7, 2020

Amazing

By Lahcene O M

Mar 3, 2020

Great

By tale p

Jun 27, 2020

good

By p s

Jun 24, 2020

Nice

By ANURAG P

Jun 5, 2020

generally while using scikit-learn library for logistic regression, we don't really understand the classes and alogoriths behind what we import. This gives a clear view of what goes behind the imported scikit modules. Its pretty hard though as compared to sckit learn code but gives some deep knowledge about the numpy library

By Munna K

Sep 27, 2020

Well..I would like to recommend this project for machine learning students who can have a better understanding of concepts related to deep learning and Ml.

By Chow K M

Oct 4, 2021

I​t's implementation of gradient descent without the theory. Without the theory, it would not be understandable.

By Manzil-e A K

Jul 20, 2020

I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.

By Rosario P

Sep 23, 2020

Good course, very simple to understand

By Abdul Q

Apr 30, 2020

For beginners this course is great.

By Weerachai Y

Jul 8, 2020

thanks

By Александр П

Mar 9, 2020

бестолковый курс, виртуальный стол неудобный, ноутбук неполный, нет модуля helpers

By Haofei M

Mar 4, 2020

totally waste of time. please go to enrol Anderw Ng courses about deep learning.