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Logistic Regression with Python and Numpy(으)로 돌아가기

Coursera Project Network의 Logistic Regression with Python and Numpy 학습자 리뷰 및 피드백

4.5
별점
99개의 평가
18개의 리뷰

강좌 소개

In this 2-hour long project-based course, you will learn how to implement Logistic Regression using Python and Numpy. Logistic Regression is an important fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of logistic regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training and validation process. Since this is a practical, project-based course, you will need to have a theoretical understanding of logistic regression, and gradient descent. We will focus on the practical aspect of implementing logistic regression with gradient descent, but not on the theoretical aspect. By the end of this course, you would create and train a logistic model that will be able to predict if a given image is of hand-written digit zero or of hand-written digit one. The model will be able to distinguish between images or 0s and 1s, and it will do that with a very high accuracy. Not only that, your implementation of the logistic model will also be able to solve any generic binary classification problem. You will still have to train model instances on specific datasets of course, but you won’t have to change the implementation and it will be re-usable. The dataset for images of hand written digits comes from the popular MNIST dataset. This data set consists of images for the 10 hand-written digits (from 0 to 9), but since we are implementing logistic regression, and are looking to solve binary classification problems - we will work with examples of hand written zeros, and hand written ones and we will ignore examples of rest of the digits. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

최상위 리뷰

DP

Apr 09, 2020

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

MT

Mar 10, 2020

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

필터링 기준:

Logistic Regression with Python and Numpy의 18개 리뷰 중 1~18

교육 기관: shiva s t

Mar 09, 2020

it is a great course and successfully trained my ml model

교육 기관: Duddela S P

Apr 09, 2020

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

교육 기관: Megan T

Mar 10, 2020

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

교육 기관: Raj K

Apr 29, 2020

Great learning material and hands-on platform!

교육 기관: Pranjal M

Jun 14, 2020

A very good project for learners

교육 기관: Gangone R

Jul 02, 2020

very useful course

교육 기관: JONNALA S R

May 07, 2020

Good Initiation

교육 기관: Nandivada P E

Jun 15, 2020

super course

교육 기관: Doss D

Jun 23, 2020

Thank you

교육 기관: Lahcene O M

Mar 03, 2020

Great

교육 기관: tale p

Jun 27, 2020

good

교육 기관: p s

Jun 24, 2020

Nice

교육 기관: ANURAG P

Jun 05, 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

교육 기관: Manzil-e A K

Jul 20, 2020

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

교육 기관: Abdul Q

Apr 30, 2020

For beginners this course is great.

교육 기관: Weerachai Y

Jul 08, 2020

thanks

교육 기관: Александр П

Mar 09, 2020

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

교육 기관: Haofei M

Mar 05, 2020

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