machine learning index | 机器学习资料索引

00 [机器学习资料索引].md

机器学习资料索引

demo-python.ipynb

machinelearning-index.ipynb

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      "source": "# 机器学习索引"
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      "source": "## 数学资料"
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      "source": "* Familiarity with the basic probability theory. ([CS109](https://youtu.be/1mUzQZGwgVs?list=PLb4G5axmLqiuneCqlJD2bYFkBwHuOzKus) or Stat116 is sufficient but not necessary.)\n* Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)\n* [线性代数 (mit)](https://youtu.be/ZK3O402wf1c?list=PL49CF3715CB9EF31D)\n* [概率(mit)](https://youtu.be/j9WZyLZCBzs?list=PLUl4u3cNGP60A3XMwZ5sep719_nh95qOe)\n"
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      "source": "## 相关资料"
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      "source": "* [Coursera Machine Learning (Andrew Ng)](https://www.coursera.org/learn/machine-learning/)\n* [udacity Google的深度学习](https://www.udacity.com/me#!/)\n* [Machine Learning Course Materials](http://cs229.stanford.edu/materials.html)\n* [previous version of the course, video and material 可以提前看视频，以及资料视频下载](https://class.coursera.org/ml-003/lecture)\n* [previous version of the course, 第五版](https://class.coursera.org/ml-005/lecture)\n* [Andrew NG 的openclassroom课程 ](http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning)\n* [stanford UFLDL教程](http://ufldl.stanford.edu/wiki/index.php/UFLDL教程)\n* [freemind关于ML的博客](http://freemind.pluskid.org/category#machine-learning)\n* [octave wiki](http://wiki.octave.org/Main_Page)\n* [ML笔记:my octave exercises for 2011 stanford machine learning class, posted after the due date of course](https://github.com/schneems/Octave)\n* [ML笔记：Programming Exercises on http://ml-class.org http://github.com/everpeace/ml-class-assignments](https://github.com/everpeace/ml-class-assignments)\n* [ Stanford Machine Learning 公开课笔记 5篇博客](http://blog.csdn.net/feliciafay/article/category/679349)\n* [jerrylead 相关机器学习标签的博客](http://www.cnblogs.com/jerrylead/tag/Machine%20Learning/)\n* [machine learning on WikiPedia](https://en.wikipedia.org/wiki/Machine_learning)\n* [python 基础学习, the hard way](http://learnpythonthehardway.org/book/)\n* [[Machine Learning] 机器学习常见算法分类汇总](http://www.cnblogs.com/maybe2030/p/4665816.html)\n"
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      "source": "## NG课程 编写代码注意事项 \n\n1. 不要写死数组的长度，要求具有通用性，以便适应任何size\n2. 在每一行的结尾使用分好 ; \n\nQ: I'm coming up against some problems when trying to implement this, particularly for grad(2:end). Tips would be much appreciated! I believe that my code is correct, but it fails when I submit it! When I run the exN script, the answers match those shown by the script or in the PDF file. But when I submit my code, the grader does not give me any points for several of the sections of the exercise. What should I do? A: It is important to realize that the grader uses test cases that are different from those in the PDF file and in the exercise scripts. One type of error that frequently causes submit failures is making any assumptions in your implementation about the dimensions of the objects (e.g. number of features or number of input training examples). Your code should be general in the sense that it can handle objects of any size.\n\nTips for debugging your code include adding print statements to show the intermediate results being generated in your computations. In Octave and Matlab, removing the semicolon from a line of code will cause the result of that line to be printed. This can be a useful technique for seeing how your code is working. Check the results using a calculator and compare to the formulas that the code is intended to implement. There is also the \"keyboard()\" function that lets you stop your code and examine the values of the variables. See \"help keyboard\" for more information."
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      "cell_type": "markdown",
      "source": "## 机器学习的相关算法"
    },
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      "cell_type": "markdown",
      "source": "## 机器学习案例 | 应用"
    },
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      "metadata": {},
      "cell_type": "markdown",
      "source": "## 机器学习相关术语"
    },
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      "cell_type": "markdown",
      "source": "## 抓取coursera machine learning课程subtitles"
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      "source": "```python\nimport urllib2\nimport os\nurl = 'https://class.coursera.org/ml-005/lecture/subtitles?q='\n#url2 = '_en&format=srt'\nurl2 = '_en&format=txt'\nprint \"Downloaded Subtitle No. \", \nfor i in range(1,115):\n    try:\n        remoteurl = urllib2.urlopen(url+str(i)+url2)\n        cd = remoteurl.info()['Content-Disposition'].split(';')\n        cd2 = cd[1].split('=')[1].strip(\"\\\"'\")\n        filename = urllib2.unquote(cd2).replace(\"/\",\"_\")\n        print \"filename:\", filename\n\n    \n        page = remoteurl.read();\n\n        f =  open('subtitle/'+filename,'w+');\n        f.write(page);\n        f.close();\n        print i,;\n    except (urllib2.HTTPError, urllib2.URLError) as e:\n        print '\\nProblem Downloading file: ',i\n        print 'OR Connection Error'\n\nprint '\\nDownload Finished\\nHappy Coding!!!'\n```"
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