├── LICENSE
├── README-CN.md
├── README.md
└── Screenshot
    └── 20180115161718.png


/LICENSE:
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/README-CN.md:
--------------------------------------------------------------------------------
  1 | # ML-Roadmap
  2 | Roadmap to becoming a Machine Learning developer in 2020
  3 | 
  4 | <div align="center">
  5 | <img src="https://github.com/JsonChao/ML-Roadmap/blob/master/Screenshot/20180115161718.png">
  6 | </div>
  7 | 
  8 | 
  9 | ## 入门
 10 | 
 11 | [机器学习如此有趣!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471)
 12 | （[中文版](https://zhuanlan.zhihu.com/p/24339995)）
 13 | 
 14 | [数据科学基础](https://becominghuman.ai/data-science-simplified-principles-and-process-b06304d63308)
 15 | 
 16 | ## 数据分析
 17 | 
 18 | ### 概念：
 19 | 
 20 | [The Foundations of Data Science](http://data8.org/)
 21 | 
 22 | [Computational and Inferential Thinking -The Foundations of Data Science](https://www.inferentialthinking.com/)
 23 | 
 24 | 
 25 | ### 数据挖掘算法：
 26 | 
 27 | [LearnDataScience](https://github.com/nborwankar/LearnDataScience)
 28 | （[算法实现](https://github.com/donnemartin/data-science-ipython-notebooks#scikit-learn)）
 29 | 
 30 | [资源列表](https://www.datascienceweekly.org/data-science-resources/the-big-list-of-data-science-resources)
 31 | 
 32 | ## 机器学习
 33 | 
 34 | [吴恩达机器学习](https://www.coursera.org/learn/machine-learning)
 35 | [中文版](http://open.163.com/special/opencourse/machinelearning.html)
 36 | 
 37 | [Google机器学习速成课程](https://developers.google.cn/machine-learning/crash-course/)
 38 | 
 39 | [卡内基梅隆大学](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)
 40 | 
 41 | ## 深度学习
 42 | 
 43 | [吴恩达深度学习](https://mooc.study.163.com/smartSpec/detail/1001319001.htm)
 44 | 
 45 | [谷歌深度学习](https://www.udacity.com/course/deep-learning--ud730)
 46 | 
 47 | ## 数据学习
 48 | 
 49 | [Learning From Data](http://work.caltech.edu/lectures.html)
 50 | 
 51 | ## 神经网络
 52 | 
 53 | [Youtube-Neural Networks](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
 54 | 
 55 | ## 大学课程
 56 | 
 57 | ### 斯坦福大学：
 58 | 
 59 | [《统计学学习》](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)
 60 | 
 61 | [《机器学习》](http://cs229.stanford.edu/)
 62 | 
 63 | [《卷积神经网络》](http://cs231n.stanford.edu/)
 64 | 
 65 | [《深度学习之自然语言处理》](http://cs224d.stanford.edu/)
 66 | 
 67 | ### 麻省理工大学：
 68 | 
 69 | [《神经网络介绍》](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-641j-introduction-to-neural-networks-spring-2005/index.htm)
 70 | 
 71 | [《机器学习》](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/)
 72 | 
 73 | [《预测》](http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/index.htm)
 74 | 
 75 | ### 更多课程：
 76 | 
 77 | [awesome-machine-learning](https://github.com/RatulGhosh/awesome-machine-learning)
 78 | 
 79 | ## 图书
 80 | 
 81 | ### 机器学习：
 82 | 
 83 | [Pattern Recognition and Machine Learning](https://book.douban.com/subject/2061116/)
 84 | [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)
 85 | [Matlab实现](https://github.com/PRML/PRMLT)
 86 | 
 87 | [Introduction to Machine Learning with Python](https://book.douban.com/subject/26279609/)
 88 | 
 89 | [Hands-On Machine Learning with Scikit-Learn and TensorFlow](https://book.douban.com/subject/26840215/)
 90 | 
 91 | ### 手册：
 92 | 
 93 | [understanding-machine-learning-theory-algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)
 94 | 
 95 | [ESLII](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)
 96 | 
 97 | 
 98 | ### 深度学习：
 99 | 
100 | [《深度学习》](https://book.douban.com/subject/27087503/)
101 | [官网](http://www.deeplearningbook.org/)
102 | [中文](https://github.com/exacity/deeplearningbook-chinese)
103 | 
104 | ### 神经网络：
105 | 
106 | [入门书](http://neuralnetworksanddeeplearning.com/)
107 | [中文](https://tigerneil.gitbooks.io/neural-networks-and-deep-learning-zh/content/)
108 | 
109 | ## 文章/小视频
110 | 
111 | [Google TensorFlow](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
112 | 
113 | [Python Machine](https://pythonprogramming.net/machine-learning-tutorial-python-introduction/)
114 | 
115 | [算法](https://medium.com/machine-learning-101)
116 | 
117 | [机器学习](https://medium.com/machine-learning-for-humans)
118 | 
119 | [Python入门深度学习完整指南](https://juejin.im/post/5a4666b6f265da4327188f2c)
120 | 
121 | [最佳实践](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
122 | 
123 | ## 博客
124 | 
125 | [machinelearningmastery](https://machinelearningmastery.com/blog/)
126 | 
127 | [iamtrask](http://iamtrask.github.io/)
128 | 
129 | [深度学习](http://www.deeplearningbook.org/)
130 | 
131 | [Python自然语言处理](http://www.nltk.org/book/)
132 | 
133 | [教程列表](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)
134 | 
135 | ### 神经网络：
136 | 
137 | [神经网络](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
138 | 
139 | [rnn-effectiveness](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
140 | 
141 | [NN-Manifolds-Topology](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)
142 | 
143 | [Understanding-LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
144 | 
145 | [augmented-rnns](http://distill.pub/2016/augmented-rnns/)
146 | 
147 | [spotify-cnns](http://benanne.github.io/2014/08/05/spotify-cnns.html)
148 | 
149 | ## 算法
150 | 
151 | ### 监督式学习：
152 | 
153 | [决策树](https://en.wikipedia.org/wiki/Decision_tree)
154 | 
155 | [朴素贝叶斯分类器](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
156 | 
157 | [最小二乘法](https://en.wikipedia.org/wiki/Ordinary_least_squares)
158 | 
159 | [逻辑回归](https://en.wikipedia.org/wiki/Logistic_regression)
160 | 
161 | [支持向量机](https://en.wikipedia.org/wiki/Support_vector_machine)
162 | 
163 | [集成方法](https://en.wikipedia.org/wiki/Ensemble_learning)
164 | 
165 | ### 无监督式学习：
166 | 
167 | [聚类算法](https://en.wikipedia.org/wiki/Cluster_analysis)
168 | 
169 | ### 5个著名的聚类算法：
170 | 
171 | [K-Means](https://en.wikipedia.org/wiki/K-means_clustering)
172 | 
173 | [Mean-Shift](https://en.wikipedia.org/wiki/Mean_shift)
174 | 
175 | [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)
176 | 
177 | [EM/GMM](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
178 | 
179 | [Agglomerative Hierarchical](https://en.wikipedia.org/wiki/Hierarchical_clustering)
180 | 
181 | ### 其它：
182 | 
183 | [主成分分析](https://en.wikipedia.org/wiki/Principal_component_analysis)
184 | 
185 | [奇异值分解](https://en.wikipedia.org/wiki/Singular-value_decomposition)
186 | 
187 | [独立成分分析](https://en.wikipedia.org/wiki/Independent_component_analysis)
188 | 
189 | ### 更多算法列表：
190 | 
191 | [Machine learning algorithms](https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms)
192 | 
193 | [a-tour-of-machine-learning-algorithms](https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
194 | 
195 | ### Scikit-Learn算法文档：
196 | 
197 | [supervised_learning](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning)
198 | 
199 | [clustering](http://scikit-learn.org/stable/modules/clustering.html#clustering)
200 | 
201 | [decomposition](http://scikit-learn.org/stable/modules/decomposition.html#decompositions)
202 | 
203 | [model_selection](http://scikit-learn.org/stable/model_selection.html#model-selection)
204 | 
205 | [preprocessing](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing)
206 | 
207 | ## 实践项目
208 | 
209 | [8个有趣的项目](https://elitedatascience.com/machine-learning-projects-for-beginners)
210 | 
211 | [公共数据](https://github.com/awesomedata/awesome-public-datasets)
212 | 
213 | ## 更多进阶资源
214 | 
215 | [awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning)
216 | 
217 | [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)
218 | 
219 | [awesome-deeplearning-resources](https://github.com/endymecy/awesome-deeplearning-resources)
220 | 
221 | 
222 | ### About me
223 | 
224 | - #### Email:[chao.qu521@gmail.com]()
225 | - #### Blog:[https://jsonchao.github.io/](https://jsonchao.github.io/)
226 | - #### 掘金:[https://juejin.im/user/5a3ba9375188252bca050ade](https://juejin.im/user/5a3ba9375188252bca050ade)
227 |     
228 | ### License
229 | 
230 | Copyright 2018 JsonChao
231 | 
232 | Licensed under the Apache License, Version 2.0 (the "License");
233 | you may not use this file except in compliance with the License.
234 | You may obtain a copy of the License at
235 | 
236 |    http://www.apache.org/licenses/LICENSE-2.0
237 | 
238 | Unless required by applicable law or agreed to in writing, software
239 | distributed under the License is distributed on an "AS IS" BASIS,
240 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
241 | See the License for the specific language governing permissions and
242 | limitations under the License.
243 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # ML-Roadmap
  2 | Roadmap to becoming a Machine Learning developer in 2020
  3 | 
  4 | <div align="center">
  5 | <img src="https://github.com/JsonChao/ML-Roadmap/blob/master/Screenshot/20180115161718.png">
  6 | </div>
  7 | 
  8 | ## Translations
  9 | 
 10 | - [简体中文](https://github.com/JsonChao/ML-Roadmap/blob/master/README-CN.md)
 11 | 
 12 | ## Introduction
 13 | 
 14 | [Machine learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471)
 15 | 
 16 | 
 17 | [Data Science Simplified](https://becominghuman.ai/data-science-simplified-principles-and-process-b06304d63308)
 18 | 
 19 | ## Data Analysis
 20 | 
 21 | ### Concepts：
 22 | 
 23 | [The Foundations of Data Science](http://data8.org/)
 24 | 
 25 | [Computational and Inferential Thinking -The Foundations of Data Science](https://www.inferentialthinking.com/)
 26 | 
 27 | ### Data Mining Algorithm：
 28 | 
 29 | [LearnDataScience](https://github.com/nborwankar/LearnDataScience)
 30 | [Algorithm Implementation](https://github.com/donnemartin/data-science-ipython-notebooks#scikit-learn)
 31 | 
 32 | [Data-science-resources](https://www.datascienceweekly.org/data-science-resources/the-big-list-of-data-science-resources)
 33 | 
 34 | ## Machine Learning
 35 | 
 36 | [Andrew Ng-Machine Learning](https://www.coursera.org/learn/machine-learning)
 37 | 
 38 | [Google-Machine Learning Crash Course](https://developers.google.cn/machine-learning/crash-course/)
 39 | 
 40 | [Carnegie Mellon University-Machine Learning](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)
 41 | 
 42 | ## Deep Learning
 43 | 
 44 | [Udacity-Deep Learning](https://www.udacity.com/course/deep-learning--ud730)
 45 | 
 46 | ## Learning From Data
 47 | 
 48 | [Learning From Data](http://work.caltech.edu/lectures.html)
 49 | 
 50 | ## Neural Networks
 51 | 
 52 | [Youtube-Neural Networks](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
 53 | 
 54 | ## University Course
 55 | 
 56 | ### Stanford University：
 57 | 
 58 | [《Statistical Learning》](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)
 59 | 
 60 | [《Machine Learning》](http://cs229.stanford.edu/)
 61 | 
 62 | [《Convolutional Neural Networks》](http://cs231n.stanford.edu/)
 63 | 
 64 | [《Deep Leanring for Natural Language Processing》](http://cs224d.stanford.edu/)
 65 | 
 66 | ### MIT University：
 67 | 
 68 | [《Introduction to neural networks》](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-641j-introduction-to-neural-networks-spring-2005/index.htm)
 69 | 
 70 | [《Machine Learning》](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/)
 71 | 
 72 | [《Prediction》](http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/index.htm)
 73 | 
 74 | ### More Courses：
 75 | 
 76 | [awesome-machine-learning](https://github.com/RatulGhosh/awesome-machine-learning)
 77 | 
 78 | ## Books
 79 | 
 80 | ### Machine Learning：
 81 | 
 82 | [Pattern Recognition and Machine Learning](https://book.douban.com/subject/2061116/)
 83 | [PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)
 84 | [Matlab Implementation](https://github.com/PRML/PRMLT)
 85 | 
 86 | [Introduction to Machine Learning with Python](https://book.douban.com/subject/26279609/)
 87 | 
 88 | [Hands-On Machine Learning with Scikit-Learn and TensorFlow](https://book.douban.com/subject/26840215/)
 89 | 
 90 | ### Manual：
 91 | 
 92 | [understanding-machine-learning-theory-algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)
 93 | 
 94 | [ESLII](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)
 95 | 
 96 | ### Deep Learning：
 97 | 
 98 | [《Deep Learning》](https://book.douban.com/subject/27087503/)
 99 | 
100 | [Official website](http://www.deeplearningbook.org/)
101 | 
102 | ### Neural Networks：
103 | 
104 | [Introduction-Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)
105 | 
106 | 
107 | ## Articles/Small Videos
108 | 
109 | [Google TensorFlow](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
110 | 
111 | [Machine-learning-tutorial-python-introduction](https://pythonprogramming.net/machine-learning-tutorial-python-introduction/)
112 | 
113 | [machine-learning-algorithm](https://medium.com/machine-learning-101)
114 | 
115 | [machine-learning-for-humans](https://medium.com/machine-learning-for-humans)
116 | 
117 | [Rules of Machine Learning:
118 | Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)
119 | 
120 | ### Blogs：
121 | 
122 | [machinelearningmastery](https://machinelearningmastery.com/blog/)
123 | 
124 | [iamtrask](http://iamtrask.github.io/)
125 | 
126 | [Deeplearningbook](http://www.deeplearningbook.org/)
127 | 
128 | [Natural Language Processing with Python](http://www.nltk.org/book/)
129 | 
130 | [Machine-Learning-Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)
131 |     
132 | ### Neural Networks：
133 | 
134 | [Youtube-Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
135 | 
136 | [rnn-effectiveness](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
137 | 
138 | [NN-Manifolds-Topology](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)
139 | 
140 | [Understanding-LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
141 | 
142 | [augmented-rnns](http://distill.pub/2016/augmented-rnns/)
143 | 
144 | [spotify-cnns](http://benanne.github.io/2014/08/05/spotify-cnns.html)
145 | 
146 | ## Algorithms
147 | 
148 | ### Supervised Learning：
149 | 
150 | [Decision_tree](https://en.wikipedia.org/wiki/Decision_tree)
151 | 
152 | [Naive_Bayes_classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
153 | 
154 | [Ordinary_least_squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
155 | 
156 | [Logistic_regression](https://en.wikipedia.org/wiki/Logistic_regression)
157 | 
158 | [Support_vector_machine](https://en.wikipedia.org/wiki/Support_vector_machine)
159 | 
160 | [Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning)
161 | 
162 | ### Unsupervised Learning：
163 | 
164 | [Cluster_analysis](https://en.wikipedia.org/wiki/Cluster_analysis)
165 | 
166 | ### Five Famous clustering algorithms：
167 | 
168 | [K-Means](https://en.wikipedia.org/wiki/K-means_clustering)
169 | 
170 | [Mean-Shift](https://en.wikipedia.org/wiki/Mean_shift)
171 | 
172 | [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)
173 | 
174 | [EM/GMM](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
175 | 
176 | [Agglomerative Hierarchical](https://en.wikipedia.org/wiki/Hierarchical_clustering)
177 | 
178 | ### Others：
179 | 
180 | [Principal_component_analysis](https://en.wikipedia.org/wiki/Principal_component_analysis)
181 | 
182 | [Singular-value_decomposition](https://en.wikipedia.org/wiki/Singular-value_decomposition)
183 | 
184 | [Independent_component_analysis](https://en.wikipedia.org/wiki/Independent_component_analysis)
185 | 
186 | ### More Algorithm Lists：
187 | 
188 | [Machine learning algorithms](https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms)
189 | 
190 | [a-tour-of-machine-learning-algorithms](https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
191 | 
192 | ### Scikit-Learn Algorithm for document：
193 | 
194 | [supervised_learning](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning)
195 | 
196 | [clustering](http://scikit-learn.org/stable/modules/clustering.html#clustering)
197 | 
198 | [decomposition](http://scikit-learn.org/stable/modules/decomposition.html#decompositions)
199 | 
200 | [model_selection](http://scikit-learn.org/stable/model_selection.html#model-selection)
201 | 
202 | [preprocessing](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing)
203 | 
204 | ## Practice Project
205 | 
206 | [machine-learning-projects-for-beginners](https://elitedatascience.com/machine-learning-projects-for-beginners)
207 | 
208 | [awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets)
209 | 
210 | ## More Advanced Resources
211 | 
212 | [awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning)
213 | 
214 | [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)
215 | 
216 | [awesome-deeplearning-resources](https://github.com/endymecy/awesome-deeplearning-resources)
217 | 
218 | ### About me
219 | 
220 | - #### Email:[chao.qu521@gmail.com]()
221 | - #### Blog:[https://jsonchao.github.io/](https://jsonchao.github.io/)
222 |     
223 | ### License
224 | 
225 | Copyright 2018 JsonChao
226 | 
227 | Licensed under the Apache License, Version 2.0 (the "License");
228 | you may not use this file except in compliance with the License.
229 | You may obtain a copy of the License at
230 | 
231 |    http://www.apache.org/licenses/LICENSE-2.0
232 | 
233 | Unless required by applicable law or agreed to in writing, software
234 | distributed under the License is distributed on an "AS IS" BASIS,
235 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
236 | See the License for the specific language governing permissions and
237 | limitations under the License.
238 | 


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