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Logistic Regression with NumPy and Python に戻る

Coursera Project Network による Logistic Regression with NumPy and Python の受講者のレビューおよびフィードバック



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....



May 24, 2020

Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.


Jun 09, 2020

I really enjoyed this course. Thank you for your valuable teaching.


Logistic Regression with NumPy and Python: 1 - 25 / 28 レビュー

by Chinmay B

May 24, 2020

Its a good course. Instructor is good. Lot of concepts cleared and enough practice has done.

by Juan M B

Jun 07, 2020

Great tool to practice what i learned in Andrew Yng's ML course about Log. Reg.

by Ramya G R

Jun 09, 2020

I really enjoyed this course. Thank you for your valuable teaching.


Apr 04, 2020

Thank You... Very nice and valuable knowledge provided.

by Mariappan M

May 15, 2020

Clear explanation and good content. Thanks

by Pulkit S

Jun 18, 2020

good project got to learn a lot of things

by Melissa d C S

Jun 22, 2020

Please, keep doing good job

by Pritam B

May 15, 2020

it was an nice experience

by Shreyas R

Apr 25, 2020

Amazing. Must do this

by Diego R G

May 22, 2020

Great project!

by jagadeeswari N

May 29, 2020

nice overview

by Anisetti S K

Apr 23, 2020

well balanced

by Ayesha N

Jun 16, 2020

its was good

by Nandivada P E

Jun 15, 2020

Nice course

by Dipak S s

Apr 24, 2020

fine courxe

by p s

Jun 12, 2020


by Yurii S

Jun 09, 2020


by tale p

Jun 26, 2020


by Yogesh P

Jun 14, 2020

I have just started learning machine learning and I found out that, to brush up my foundational skills, this project was just the right one for me. The explanations are spot on and the learning experience was also quite fruitful. Highly recommended.

by Mukulesh S

Apr 02, 2020

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.

by Zaheer U R

Jun 01, 2020

Very Interesting and useful course. It helped me gain additional values and techniques about logistic regression

by Alama N

May 31, 2020

Thank you for formation freind

by Girish G A

May 23, 2020

If you are looking for hands on projects after completing Andrew NG Machine Learning Courses, these courses are more of a revision. No explanation about the plots and its parameters. Why it's 0 1 or 2. It would have been nice had there been more explanation about plotting and data visualization. Also accuracy calculated at the end of course seems wrong.

by Boyuzhu

Jun 29, 2020

The code on Ryme is not clearly explained. I feel the lecture is a bit of confusing. We expect to know not only what code we need to write, but also why we write these codes.

by Rohan B

Jun 16, 2020

A bad course, pretty useless if you're not already well versed with logistic regression. And you need to be an expert in python data science libraries too to understanding anything at all. The test taken in the end was like a joke.