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Volver a Logistic Regression with NumPy and Python

Opiniones y comentarios de aprendices correspondientes a Logistic Regression with NumPy and Python por parte de Coursera Project Network

4.5
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262 calificaciones
34 revisiones

Acerca del Curso

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....

Principales revisiones

CB

May 24, 2020

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

RR

Jun 09, 2020

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

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1 - 25 de 34 revisiones para Logistic Regression with NumPy and Python

por Sambhaw S

Aug 02, 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

por Chinmay B

May 24, 2020

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

por Juan M B

Jun 07, 2020

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

por Ramya G R

Jun 09, 2020

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

por Punam P

Apr 04, 2020

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

por Mariappan M

May 15, 2020

Clear explanation and good content. Thanks

por Pulkit S

Jun 18, 2020

good project got to learn a lot of things

por Shruti S

Jul 21, 2020

Great course ! very informative

Thanks :)

por Melissa d C S

Jun 22, 2020

Please, keep doing good job

por Erick M A

Jul 20, 2020

Excelente aprovechamiento

por Pritam B

May 15, 2020

it was an nice experience

por Shreyas R

Apr 25, 2020

Amazing. Must do this

por Diego R G

May 22, 2020

Great project!

por jagadeeswari N

May 29, 2020

nice overview

por Anisetti S K

Apr 23, 2020

well balanced

por Ayesha N

Jun 16, 2020

its was good

por Lê Đ D

Jul 13, 2020

Really good

por Nandivada P E

Jun 15, 2020

Nice course

por Dipak S s

Apr 24, 2020

fine courxe

por PRAVEEN K K S

Jul 06, 2020

NIICE

por p s

Jun 12, 2020

Super

por Yurii S

Jun 09, 2020

GREAT

por tale p

Jun 26, 2020

good

por 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.

por 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.