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Avis et commentaires pour d'étudiants pour Logistic Regression with Python and Numpy par Coursera Project Network

4.5
étoiles
139 évaluations
24 avis

À propos du cours

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....

Meilleurs avis

DP
8 avr. 2020

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

MT
9 mars 2020

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

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1 - 24 sur 24 Avis pour Logistic Regression with Python and Numpy

par shiva s t

9 mars 2020

it is a great course and successfully trained my ml model

par Duddela S P

9 avr. 2020

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

par Megan T

10 mars 2020

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

par Raj K

29 avr. 2020

Great learning material and hands-on platform!

par Pranjal M

14 juin 2020

A very good project for learners

par Thomas H

12 nov. 2021

great hand-on training

par Ashwin K

2 sept. 2020

An amazing Project

par Gangone R

2 juil. 2020

very useful course

par JONNALA S R

7 mai 2020

Good Initiation

par Nandivada P E

15 juin 2020

super course

par Doss D

23 juin 2020

Thank you

par Saikat K 1

7 sept. 2020

Amazing

par Lahcene O M

3 mars 2020

Great

par tale p

27 juin 2020

good

par p s

24 juin 2020

Nice

par ANURAG P

5 juin 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

par Munna K

27 sept. 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.

par Chow K M

4 oct. 2021

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

par Manzil-e A K

20 juil. 2020

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

par Rosario P

23 sept. 2020

Good course, very simple to understand

par Abdul Q

30 avr. 2020

For beginners this course is great.

par Weerachai Y

8 juil. 2020

thanks

par Александр П

9 mars 2020

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

par Haofei M

4 mars 2020

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