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.

提供方

## Project: Logistic Regression with Python and Numpy

## 课程信息

### 您将学到的内容有

Implement Logistic Regression using Python and Numpy.

Apply Logistic Regression to solve binary classification problems.

### 您将获得的技能

#### 可分享的证书

#### 100% 在线

#### 可灵活调整截止日期

#### 中级

Some programming experience in Python is preferred. Understanding of the theory behind logistic regression, gradient descent is required.

#### 完成时间大约为4 小时

#### 英语（English）

### 提供方

#### Rhyme

Rhyme is Coursera's hands-on project-based learning platform. On Rhyme, learners get instant access to pre-configured cloud desktops containing all the software and data they need. Rhyme helps learners apply the knowledge they learned in other Coursera courses into specific tools and use-cases. So they become fully prepared to solve problems in the real-world!

## 教学大纲 - 您将从这门课程中学到什么

**完成时间为 2 小时**

## Deep Learning Fundamentals: Logistic Regression

Welcome to this project-based course on Logistic Regression. 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 in Deep Learning, and even though popular machine learning frameworks have implementations of logistic regression available, learning to implement it on your own will enable you to understand the mechanics of optimization algorithm and the training and validation process. 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 zeros and ones, 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.

**完成时间为 2 小时**

**2 个阅读材料**

**1 个练习**

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