# machine-learning-resourses **Repository Path**: hotheart1982/machine-learning-resourses ## Basic Information - **Project Name**: machine-learning-resourses - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-12 - **Last Updated**: 2026-01-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # machine-learning-resourses ## 机器学习 #### 综述 机器学习是人工智能的一个分支。人工智能的研究历史有着一条从以“推理”为重点，到以“知识”为重点，再到以“学习”为重点的自然、清晰的脉络。显然，机器学习是实现人工智能的一个途径，即以机器学习为手段解决人工智能中的问题。机器学习在近30多年已发展为一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、计算复杂性理论等多门学科。机器学习理论主要是设计和分析一些让计算机可以自动“学习”的算法。 ["The introduction to machine learning"](https://www.youtube.com/watch?v=PPLop4L2eGk) Andrew NG #### 书籍 * 数学方面 [《Math for ML book》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/mml-book.pdf)Math for machine learning book by Faisal and Ong [《数学之美》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/数学之美.pdf) by 吴军 * 机器学习 以下两本为国内非常著名的机器学习书，入门学习或者找工作的必备书籍 [《机器学习》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0_%E5%91%A8%E5%BF%97%E5%8D%8E.pdf)by 周志华 [《统计学习方法》](https://github.com/wayneguolei/Machine-Learning-Resourses/blob/master/books/%E7%BB%9F%E8%AE%A1%E5%AD%A6%E4%B9%A0%E6%96%B9%E6%B3%95-%E6%9D%8E%E8%88%AA-%E9%AB%98%E6%B8%85%E7%9B%AE%E5%BD%95.pdf)by 李航 非常通俗易懂的一本机器学习书 [《图解机器学习》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/%E5%9B%BE%E8%A7%A3%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0.pdf) by 杉山将 以下为机器学习领域的三本神书 [《Pattern Recognition And Machine Learning》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) by Christopher M. Bishop [《The Elements of Statistical Learning》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/The_Elements_of_Statistical%20Learning_Data_Mining_Inference_and_Prediction.pdf) by Trevor Hastie, Robert Tibshirani, Jerome Friedman [《Machine Learning A Probabilistic Perspective》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/ML%20Machine%20Learning-A%20Probabilistic%20Perspective.pdf) by Kevin P. Murphy * 深度学习方面 [深度学习（中文版）](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%20%E4%B8%AD%E6%96%87%E7%89%88.pdf) by Ian Goodfellow, Yoshua Bengio, Aaron Courville [深度学习（英文版）](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/deeplearningbook.pdf) by Ian Goodfellow, Yoshua Bengio, Aaron Courville * Python方面 [《LearningPython》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/LearningPython.pdf) [《利用Python进行数据分析》英文版](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/%E5%88%A9%E7%94%A8Python%E8%BF%9B%E8%A1%8C%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90-%E7%AC%AC2%E7%89%88%E5%9F%BA%E4%BA%8EPython3-%E8%8B%B1%E6%96%87%E7%89%88.pdf) * 大数据方面 [《Hadoop The Definitive Guide, 4th Edition》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/Hadoop%20The%20Definitive%20Guide%2C%204th%20Edition.pdf) [《Learning Spark Lightning Fast Data Analysis》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/Learning-Spark-Lightning-Fast-Data-Analysis.pdf) [《Advanced Analytics with Spark》](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/Advanced-Analytics-with%20Spark.pdf) #### 课程 * 数学课 [线性代数的本质](https://www.bilibili.com/video/av6731067/?redirectFrom=h5#page=3) by 3Blue1Brown [MIT 18.06 线性代数](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) by MIT * 机器学习课(Master Level) [机器学习](https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) by Andrew Ng Stanford University [机器学习基石](https://www.youtube.com/playlist?list=PLXVfgk9fNX2I7tB6oIINGBmW50rrmFTqf) by 林轩田 台大 [机器学习技法](https://www.youtube.com/playlist?list=PLXVfgk9fNX2IQOYPmqjqWsNUFl2kpk1U2) by 林轩田 台大 [机器学习速成课程](https://developers.google.com/machine-learning/crash-course/ml-intro?hl=zh-cn) by Google [机器学习](https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.102x+2T2018/course/) by ColumbiaX CSMM.102X * 机器学习课(Phd Level) [Advanced Introduction to Machine Learning](http://www.cs.cmu.edu/~bapoczos/Classes/ML10715_2015Fall/index.html) by cmu [(视频)](https://www.youtube.com/playlist?list=PL4DwY1suLMkcu-wytRDbvBNmx57CdQ2pJ&jct=q4qVgISGxJql7TlE6eSLKa8Wwci8SA&disable_polymer=true) [Large Scale ML](http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/) by Toronto [Statistical Learning Theory and Applications](http://www.mit.edu/~9.520/fall15/index.html) by MIT * 深度学习课 [深度学习专项课程](https://www.coursera.org/specializations/deep-learning) by Andrew Ng [CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/) by Stanford [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks) by Geoffrey Hinton #### 知乎回答 #### Open Sources [机器学习资源汇总(课程、教材、教程、笔记、速查等)](https://sgfin.github.io/learning-resources/) by 《ML Resources | Sam's Academic Blog》 by Samuel Finlayson [经典书《模式识别与机器学习》习题答案集](https://github.com/GoldenCheese/PRML-Solution-Manual) by ZHENGQI GAO GitHub [微软开源的Python机器学习包，具有ML.NET的功能，与Scikit-Learn高度兼容](https://github.com/Microsoft/NimbusML) by Microsoft ['pymc-learn: Practical probabilistic machine learning in Python](https://github.com/pymc-learn/pymc-learn) [提高Jupyter使用效率的工具](https://towardsdatascience.com/jupyter-tools-to-increase-productivity-7b3c6b90be09) by Gautam Borgohain [Fast.ai](https://www.fast.ai/#technical) Wonderful set of intro lectures + notebooks from Jeremy Howard and Rachel Thomas [从头开始学习机器学习](https://github.com/neerjad/MachineLearning) by Neerja Doshi [What the f*ck Python!](https://github.com/satwikkansal/wtfpython/tree/b262f2975641da1493853a390a171a051f561914#structure-of-the-examples) by satwikkansal ## 自然语言处理 #### 书籍 [Overview of Deep Learning for NLP](https://github.com/wayneguolei/machine-learning-resourses/blob/master/books/Goldber_Primer_Neural_Nets_NLP.pdf) by Goldber Primer #### 课程 [CS224N Deep Learning for NLP](https://www.youtube.com/playlist?list=PLqdrfNEc5QnuV9RwUAhoJcoQvu4Q46Lja) by Stanford [CMU CS 11-747](http://www.phontron.com/class/nn4nlp2018/schedule.html) by CMU [Oxford Deep NLP 2017](https://github.com/oxford-cs-deepnlp-2017/lectures) by Oxford and DeepMind [Tutorial: Machine Learning with Text in scikit-learn](https://github.com/wayneguolei/pycon-2016-tutorial) by Kevin Markham [YSDA Natural Language Processing course](https://github.com/yandexdataschool/nlp_course) by Elena Voita [jupyter notebook](https://mybinder.org/v2/gh/yandexdataschool/nlp_course/master) #### 知乎回答 [通过kaggle比赛学习机器学习文本分类方法](https://zhuanlan.zhihu.com/p/34899693) by 砍手豪 [Attention is all you need模型笔记](https://zhuanlan.zhihu.com/p/39034683) by 盛源车 [用深度学习（CNN RNN Attention）解决大规模文本分类问题 - 综述和实践](https://zhuanlan.zhihu.com/p/25928551) by 清凇 #### Open Sources [PyTorch-NLP - Text utilities and datasets for PyTorch](https://github.com/PetrochukM/PyTorch-NLP) by Michael Petrochuk [Attention is all you need annotated transformer Pytorch](https://github.com/harvardnlp/annotated-transformer) by HarvardNLP [Pytorch tutorial](https://github.com/yunjey/pytorch-tutorial) ## 面试 [2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) by imhuay