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學生對 Coursera Project Network 提供的 Logistic Regression with Python and Numpy 的評價和反饋

4.5
128 個評分
22 條評論

課程概述

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....

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DP
2020年4月8日

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

MT
2020年3月9日

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

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1 - Logistic Regression with Python and Numpy 的 22 個評論(共 22 個)

創建者 shiva s t

2020年3月9日

it is a great course and successfully trained my ml model

創建者 Duddela S P

2020年4月9日

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

創建者 Megan T

2020年3月10日

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

創建者 Raj K

2020年4月29日

Great learning material and hands-on platform!

創建者 Pranjal M

2020年6月14日

A very good project for learners

創建者 Ashwin K

2020年9月2日

An amazing Project

創建者 Gangone R

2020年7月2日

very useful course

創建者 JONNALA S R

2020年5月7日

Good Initiation

創建者 Nandivada P E

2020年6月15日

super course

創建者 Doss D

2020年6月23日

Thank you

創建者 Saikat K 1

2020年9月7日

Amazing

創建者 Lahcene O M

2020年3月3日

Great

創建者 tale p

2020年6月27日

good

創建者 p s

2020年6月24日

Nice

創建者 ANURAG P

2020年6月5日

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

創建者 Baquar A

2020年9月27日

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.

創建者 Manzil-e A K

2020年7月20日

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

創建者 Rosario P

2020年9月23日

Good course, very simple to understand

創建者 Abdul Q

2020年4月30日

For beginners this course is great.

創建者 Weerachai Y

2020年7月8日

thanks

創建者 Александр П

2020年3月9日

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

創建者 Haofei M

2020年3月4日

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