ML-Roadmap

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# ML-Roadmap
Roadmap to becoming a Machine Learning developer in 2020

## Translations

- [简体中文](https://github.com/JsonChao/ML-Roadmap/blob/master/README-CN.md)

## Introduction

[Machine learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471)

[Data Science Simplified](https://becominghuman.ai/data-science-simplified-principles-and-process-b06304d63308)

## Data Analysis

### Concepts：

[The Foundations of Data Science](http://data8.org/)

[Computational and Inferential Thinking -The Foundations of Data Science](https://www.inferentialthinking.com/)

### Data Mining Algorithm：

[LearnDataScience](https://github.com/nborwankar/LearnDataScience)
[Algorithm Implementation](https://github.com/donnemartin/data-science-ipython-notebooks#scikit-learn)

[Data-science-resources](https://www.datascienceweekly.org/data-science-resources/the-big-list-of-data-science-resources)

## Machine Learning

[Andrew Ng-Machine Learning](https://www.coursera.org/learn/machine-learning)

[Google-Machine Learning Crash Course](https://developers.google.cn/machine-learning/crash-course/)

[Carnegie Mellon University-Machine Learning](http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml)

## Deep Learning

[Udacity-Deep Learning](https://www.udacity.com/course/deep-learning--ud730)

## Learning From Data

[Learning From Data](http://work.caltech.edu/lectures.html)

## Neural Networks

[Youtube-Neural Networks](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)

## University Course

### Stanford University：

[《Statistical Learning》](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about)

[《Machine Learning》](http://cs229.stanford.edu/)

[《Convolutional Neural Networks》](http://cs231n.stanford.edu/)

[《Deep Leanring for Natural Language Processing》](http://cs224d.stanford.edu/)

### MIT University：

[《Introduction to neural networks》](http://ocw.mit.edu/courses/brain-and-cognitive-sciences/9-641j-introduction-to-neural-networks-spring-2005/index.htm)

[《Machine Learning》](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/)

[《Prediction》](http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/index.htm)

### More Courses：

[awesome-machine-learning](https://github.com/RatulGhosh/awesome-machine-learning)

## Books

### Machine Learning：

[Pattern Recognition and Machine Learning](https://book.douban.com/subject/2061116/)
[PDF](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)
[Matlab Implementation](https://github.com/PRML/PRMLT)

[Introduction to Machine Learning with Python](https://book.douban.com/subject/26279609/)

[Hands-On Machine Learning with Scikit-Learn and TensorFlow](https://book.douban.com/subject/26840215/)

### Manual：

[understanding-machine-learning-theory-algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)

[ESLII](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)

### Deep Learning：

[《Deep Learning》](https://book.douban.com/subject/27087503/)

[Official website](http://www.deeplearningbook.org/)

### Neural Networks：

[Introduction-Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/)

## Articles/Small Videos

[Google TensorFlow](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)

[Machine-learning-tutorial-python-introduction](https://pythonprogramming.net/machine-learning-tutorial-python-introduction/)

[machine-learning-algorithm](https://medium.com/machine-learning-101)

[machine-learning-for-humans](https://medium.com/machine-learning-for-humans)

[Rules of Machine Learning:
Best Practices for ML Engineering](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)

### Blogs：

[machinelearningmastery](https://machinelearningmastery.com/blog/)

[iamtrask](http://iamtrask.github.io/)

[Deeplearningbook](http://www.deeplearningbook.org/)

[Natural Language Processing with Python](http://www.nltk.org/book/)

[Machine-Learning-Tutorials](https://github.com/ujjwalkarn/Machine-Learning-Tutorials)
    
### Neural Networks：

[Youtube-Neural Networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)

[rnn-effectiveness](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

[NN-Manifolds-Topology](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)

[Understanding-LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

[augmented-rnns](http://distill.pub/2016/augmented-rnns/)

[spotify-cnns](http://benanne.github.io/2014/08/05/spotify-cnns.html)

## Algorithms

### Supervised Learning：

[Decision_tree](https://en.wikipedia.org/wiki/Decision_tree)

[Naive_Bayes_classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)

[Ordinary_least_squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)

[Logistic_regression](https://en.wikipedia.org/wiki/Logistic_regression)

[Support_vector_machine](https://en.wikipedia.org/wiki/Support_vector_machine)

[Ensemble_learning](https://en.wikipedia.org/wiki/Ensemble_learning)

### Unsupervised Learning：

[Cluster_analysis](https://en.wikipedia.org/wiki/Cluster_analysis)

### Five Famous clustering algorithms：

[K-Means](https://en.wikipedia.org/wiki/K-means_clustering)

[Mean-Shift](https://en.wikipedia.org/wiki/Mean_shift)

[DBSCAN](https://en.wikipedia.org/wiki/DBSCAN)

[EM/GMM](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)

[Agglomerative Hierarchical](https://en.wikipedia.org/wiki/Hierarchical_clustering)

### Others：

[Principal_component_analysis](https://en.wikipedia.org/wiki/Principal_component_analysis)

[Singular-value_decomposition](https://en.wikipedia.org/wiki/Singular-value_decomposition)

[Independent_component_analysis](https://en.wikipedia.org/wiki/Independent_component_analysis)

### More Algorithm Lists：

[Machine learning algorithms](https://en.wikipedia.org/wiki/Outline_of_machine_learning#Machine_learning_algorithms)

[a-tour-of-machine-learning-algorithms](https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)

### Scikit-Learn Algorithm for document：

[supervised_learning](http://scikit-learn.org/stable/supervised_learning.html#supervised-learning)

[clustering](http://scikit-learn.org/stable/modules/clustering.html#clustering)

[decomposition](http://scikit-learn.org/stable/modules/decomposition.html#decompositions)

[model_selection](http://scikit-learn.org/stable/model_selection.html#model-selection)

[preprocessing](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing)

## Practice Project

[machine-learning-projects-for-beginners](https://elitedatascience.com/machine-learning-projects-for-beginners)

[awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets)

## More Advanced Resources

[awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

[awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)

[awesome-deeplearning-resources](https://github.com/endymecy/awesome-deeplearning-resources)

### About me

- #### Email:[chao.qu521@gmail.com]()
- #### Blog:[https://jsonchao.github.io/](https://jsonchao.github.io/)
    
### License

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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