跟着吴恩达老师学习机器学习，Python笔记分享！-FlyAI

跟着吴恩达老师学习机器学习，Python笔记分享！

机器学习 Python3

最后更新 2020-12-22 15:06 阅读 3586

最后更新 2020-12-22 15:06

阅读 3586

机器学习 Python3

##FlyAI文献翻译

**英文原文：**[：A Full-Length Machine Learning Course in Python for Free
](https://towardsdatascience.com/a-full-length-machine-learning-course-in-python-for-free-f2732954f35f)

> **标签** ：机器学习 Python

![](https://dataset.flyai.com/桌面.png)

One of the most popular Machine-Leaning course is Andrew Ng’s machine learning course in Coursera offered by Stanford University. I tried a few other machine learning courses before but I thought he is the best to break the concepts into pieces make them very understandable.

>吴恩达在斯坦福大学讲授的机器学习课程堪称Coursera上最具人气的课程。我之前试听了好几门别的机器学习课程但我觉得他的在拆解概念使之变得易于理解方面做得最好。

But I think, there is just only one problem. That is, all the assignments and instructions are in Matlab. I am a Python user and did not want to learn Matlab. So, I just learned the concepts from the lectures and developed all the algorithms in Python.

>但是我想说这里有一个问题，那就是这门课程的所有作业和演示都是用Matlab做的。我是一个Python使用者而且我不想学Matlab.因此我只在课上学习概念然后自己用Python实现这些算法。

I explained all the algorithms in my own way(as simply as I could) and demonstrated the development of almost all the algorithms in the different articles before. I thought I should summarise them all on one page so that if anyone wants to follow, it is easier for them. Sometimes a little help goes a long way.

>我用自己的方式解释所有的算法（以我能做到的最简洁的水平）然后演示之前几篇文章的几乎所有算法开发过程。我觉得我可以把它们全部总结到一页纸的篇幅，这样可以让学的人更容易跟上一点。有时这点点帮助会起很大的作用。

If you want to take Andrew Ng’s Machine Learning course, you can audit the complete course for free as many times as you want.

>如果你想要听吴恩达的机器学习课程，你是可以免费旁听完整课程想多少次就多少次的。

###Let's dive it !

###Linear Regression(线性回归)

The most basic machine learning algorithm. This algorithm is based on the very basic straight line formula we all learned in school:

Y = AX + B

Remember? If not, no problem. This is a very simple formula. Here is the complete article that explains how this simple formula can be used to make predictions.

>最简单的机器学习算法。此算法是基于一个高中讲的很基本的直线公式：

>Y = AX + B

>没忘吧？忘了也无所谓。这是一个很简单的公式。这是解释为什么这个简单公式可以被用来做预测的完整文章。

Linear Regression Algorithm from Scratch in Python: Step by Step

>[纯手撸python线性回归算法：逐步版](https://towardsdatascience.com/basic-linear-regression-algorithm-in-python-for-beginners-c519a808b5f8 "纯手撸python线性回归算法：逐步版")

The article above works on only the datasets with a single variable. But in real life, most datasets have multiple variables. Using the same simple formula, you can develop the algorithm with multiple variables:

>上面这篇文章只在单个变量的数据集上有用。但是在现实生活中绝大多数数据集有多个变量。使用同一个简单公式你可以开发出多变量的算法。

Multivariate Linear Regression in Python Step by Step

>[python多元线性回归算法逐步演示](https://towardsdatascience.com/multivariate-linear-regression-in-python-step-by-step-128c2b127171 "python多元线性回归算法逐步演示")

###Polynomial Regression(多项式回归)

This one is also a sister of linear regression. But polynomial regression is able to find the relationship between the input variables and the output variable more precisely, even if the relationship between them is not linear:

>这个是线性回归的姊妹。但是多项式回归能够更精准地找出输入变量和输出变量之间的关系，甚至是在这个关系不是线性的时候、

Polynomial Regression From Scratch in Python

>[手撸python多项式回归](https://towardsdatascience.com/polynomial-regression-from-scratch-in-python-1f34a3a5f373 "手撸python多项式回归")

###Logistic Regression(对数回归)

Logistic regression is developed on linear regression. It also uses the same simple formula of a straight line. This is a widely used, powerful, and popular machine learning algorithm. It is used to predict a categorical variable. The following article explains the development of logistic regression step by step for binary classification:

>对数回归是在线性回归基础上开发的。他也用到了同一个简单的直线方程。这就是一个广泛使用的、强大的、流行的机器学习算法了。这可以用来预测分类变量。以下文章逐步解释了对数回归用于二分类问题的开发过程。

A Complete Logistic Regression Algorithm From Scratch in Python: Step by Step

>[完整手撸python对数回归算法：逐步版](https://towardsdatascience.com/a-complete-logistic-regression-algorithm-from-scratch-in-python-step-by-step-ce33eae7d703 "完整手撸python对数回归算法：逐步版")

Based on the concept of binary classification, it is possible to develop a logistic regression for multiclass classification. At the same time, Python has some optimization functions that help to do the calculation a lot faster. In the following article, I worked on both the methods to perform a multiclass classification task on a digit recognition dataset:

>基于二分类的概念，我们就可以开发出用于多分类任务的对数回归。同时，Python有一些优化函数可以让你快速完成计算。在下文中我从这两个思路出发完成了一个数字识别数据集的多分类任务。

Multiclass Classification Algorithm from Scratch with a Project in Python: Step by Step Guide

>[手撸python多分类算法项目实战：逐步版](https://towardsdatascience.com/multiclass-classification-algorithm-from-scratch-with-a-project-in-python-step-by-step-guide-485a83c79992 "手撸python多分类算法项目实战：逐步版")

###Neural Network(神经网络)

Neural Network has been getting more and more popular nowadays. If you are reading this article, I guess you heard of neural networks.

A neural network works much faster and much efficiently in more complex datasets. This one also involves the same formula of a straight line but the development of the algorithm is a bit more complicated than the previous ones. If you are Andrew Ng’s course, probably, you know the concepts already. Otherwise, I tried to break down the concepts as much as I could. Hopefully, it is helpful:

>神经网络如今变得越来越流行。你都读到这篇文章了，我猜你肯定听说过它。

>一个神经网络在更复杂的数据集上解决的快得多。这也涉及了同一个直线方程但是这个算法的开发要比前几个要复杂的多。如果你听了吴恩达的课，你应该已经了解了这些概念。不然，我会尽量细地拆解这个概念。希望能帮到你：

A Complete Neural Network Algorithm from Scratch in Python

>[完整手撕python神经网络算法](https://towardsdatascience.com/a-complete-neural-network-algorithm-from-scratch-in-python-fdf985f258dd "完整手撕python神经网络算法")

###Learning Curve(学习曲线)

What if you spent all that time and developed an algorithm and then, it does not work the way you wanted. How do you fix it? You need to figure out first where the problem is. Is your algorithm faulty or you need more data to train the model or you need more features? So many questions, right? But if you do not figure out the problem first and keep moving in any direction, it may kill too much time unnecessarily. Here is how you may find the problem:

>如果你把全部的时间了开发算法上但是它却不按你设想的那样去解决问题怎么办。你该怎么修好它？首先你需要找到问题出在哪里。是算法有错，还是数据不足以训练模型还是你需要更多特征？看起来问题好多哦是不是？但如果你不首先解决问题在哪，然后朝着正确的方向前进，你将会浪费无数时间。以下是如何找出问题的帮助：

If Your ML Algorithm Is Not Performing Well

>[如果你的ML算法表现不佳](https://towardsdatascience.com/your-ml-algorithm-is-not-performing-well-613dd2b07fc "如果你的ML算法表现不佳")

On the other hand, if the dataset is too skewed that is another type of challenge. For example, if you are working on a classification problem, where 95% of cases it is positive and only 5% of cases are negative. In that case, if you just randomly put all the output as positive, you are 95% correct. On the other hand, if the machine learning algorithm turns out to be 90% accurate, it is still not efficient, right? Because without a machine learning algorithm, you can predict with 95% accuracy. Here are some ideas to deal with these types of situation:

>另一方面如果数据集偏斜太厉害，那又是另一个挑战。比如你试图解决一个分类问题，95%是阳性结果只有5%是阴性结果。这么一搞你就算是瞎猜所有的预测是阳性你也有95%的准确率。另一方面，如果这个ML算法看起来有90%准确率那可真是够低效的吧？连瞎猜都比它准。下文就是几个处理这种问题的点子：

A Complete Understanding of Precision, Recall, and F Score Concepts

>[精确率，召回率，F度量概念全解](https://towardsdatascience.com/a-complete-understanding-of-precision-recall-and-f-score-concepts-23dc44defef6 "精确率，召回率，F度量概念全解")

###K Mean CLustering(K均值聚类)

One of the most popular and old unsupervised learning algorithms. This algorithm does not make predictions like the previous algorithms. It makes clusters based on the similarities amongst the data. It is more like understanding the current data more effectively. Then whenever the algorithm sees new data, based on its characteristics, it decides which cluster it belongs to. This algorithm has other importance as well. It can be used for the dimensionality reduction of images.

Why do we need dimensionality reduction of an image?

Think, when we need to input a lot of images to an algorithm to train an image classification model. Very high-resolution images could be too heavy and the training process can be too slow. In that case, a lower-dimensional picture will do the job with less time. This is just one example. You probably can imagine, there are a lot of uses for the same reason.

This article is a complete tutorial on how to develop a K mean clustering algorithm and how to use that algorithm for dimensionality reduction of an image:

A Complete K Mean Clustering Algorithm From Scratch in Python: Step by Step Guide

>最老牌的流行无监督学习算法。这个算法不想前面几个一样做出预测。他只是基于数据内部的相似性进行归类。这更像是试图更有效地理解当前的数据。然后当算法收到新数据的时候，基于它的特性，它会决定这个新数据被归类到哪一个簇。本算法还有别的重要应用。他可以用来给图片降维。

>为啥我们要给图片降维？

>你想啊，当我们需要输入大量图片进入算法来训练一个图片分类模型的时候。超高分辨率的图片会输入沉重的负担并且拖慢整个训练过程（认出这是什么图哪需要这么高分辨率！）。这种情况下一个维度更低的图片就可以让我们更快处理完。这只是举一个例子。你或许可以想见很多同样原因的别的用途。

>下文是一个开发K均值聚类算法的完整教程，以及如何使用这个算法来给图片降维。

>[完整手撕python K均值聚类算法](https://towardsdatascience.com/a-complete-k-mean-clustering-algorithm-from-scratch-in-python-step-by-step-guide-1eb05cdcd461 "完整手撕python K均值聚类算法")

###Anomaly Detection(异常检测)

Another core machine learning task. Used in credit card fraud detection, to detect faulty manufacturing or even any rare disease detection or cancer cell detection. Using the Gaussian distribution(or normal distribution) method or even more simply a probability formula it can be done. Here is a complete step by step guide for developing an anomaly detection algorithm using the Gaussian distribution concepts:

>这又是一个机器学习核心任务。用于信用卡欺诈检测，制造缺陷品检测，甚至是癌细胞检测。用高斯分布（正态分布）办法或者简单到一个概率公式就可以搞定。下文是一个完整的逐步指导文章：用高斯分布的概念来开发异常检测算法。

A Complete Anomaly Detection Algorithm From Scratch in Python: Step by Step Guide

If you need a refresher on a Gaussian distribution method, please check this one:

Univariate and Multivariate Gaussian Distribution: Clear Understanding with Visuals

>[完整手撕python异常检测算法：逐步指导](https://towardsdatascience.com/a-complete-anomaly-detection-algorithm-from-scratch-in-python-step-by-step-guide-e1daf870336e "完整手撕python异常检测算法：逐步指导")

如果你需要小小复习一下高斯分布法，看这篇文：

>[单元和多元高斯分布：清晰理解带图示](https://towardsdatascience.com/univariate-and-multivariate-gaussian-distribution-clear-understanding-with-visuals-5b85e53ea76 "单元和多元高斯分布：清晰理解带图示")

###Recommender System(推荐系统)

The recommendation system is everywhere. If you buy something on Amazon, it will recommend you some more products you may like, YouTube recommends the video you may like, Facebook recommends people you may know. So, we see it everywhere.

Andrew Ng’s course teaches how to develop a recommender system using the same formula we used in linear regression. Here is the step by step process of developing a movie recommendation algorithm:

A Complete Recommender System From Scratch in Python: Step by Step

>[完整手撕python推荐系统：逐步版](https://towardsdatascience.com/a-complete-recommender-system-from-scratch-in-python-step-by-step-6fc17a4da054 "完整手撕python推荐系统：逐步版")

###Conclusion(结语)

Hopefully, this article will help some people to start with machine learning. The best way is by doing. If you notice most of the algorithms are based on a very simple basic formula. I see a notion that machine learning or Artificial Intelligence requires very heavy programming knowledge and very difficult math. That’s not always true. With simple codes, basic math, and stats knowledge, you can go a long way. At the same time, keep improving your programming skills to do more complex tasks.

If you are interested in machine learning, just take some time and start working on it.

>希望这些文章能帮到一些人机器学习起步，最好的学习办法是跟着做，绝大多数算法都是基于一个非常简单的公式。我看到有人讲机器学习或者人工智能需要很重度的编程知识或者困难的数学，这倒未必。用简单的代码，基础数学和统计学知识你就可以做到很多事了。同时不停地提高你的编程水平从而胜任更复杂的任务。

>如果你对机器学习感兴趣就花一点时间，在这上面花点功夫。

本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可，转载请附上原文出处链接和本声明。
本文链接地址：https://flyai.com/article/793

讨论

500字

表情

发送

删除确认

是否删除该条评论？

取消删除