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Machine Learning

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# Machine Learning

## Modules
1. [Machine learning overview](./mod1/README.md)
   1. Fundamental, landscape and applications
   2. Python scientific programming
2. [Classification and regression](./mod2/README.md)
   1. Classification
   2. Regression
3. [Machine learning models](./mod3/README.md)
   1. Support vector machines
   2. Decision trees
   3. Ensemble learning and random forests
4. [Dimensionality reduction](./mod4/README.md)
   1. PCA
   2. Random projection 
   3. LLE
5. [Unsupervised learning](./mod5/README.md)
   1. k-means and DBSCAN
   2. Gaussian mixtures
6. [Neural networks and deep learning](./mod6/README.md)

# References
## Roadmap
- [Scikit-Learn: Choosing the right estimator](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)

## Textbooks
- [Introduction to Machine Learning with Python](https://github.com/amueller/introduction_to_ml_with_python)
- [HOML3: Hands-on ML with Scikit-Learn, Keras & TF 3e](https://github.com/ageron/handson-ml3)
- [Building Machine Learning Systems with Python 3e](https://github.com/PacktPublishing/Building-Machine-Learning-Systems-with-Python-Third-edition)
- [Python Machine Learning 3e](https://github.com/rasbt/python-machine-learning-book-3rd-edition)
- ---
- [Fundamentals of Machine Learning for Predictive Data Analytics](https://machinelearningbook.com/)
- [Data Mining: Practical Machine Learning Tools and Techniques](https://www.cs.waikato.ac.nz/ml/weka/book.html)
- [Machine Learning in Action](https://github.com/pbharrin/machinelearninginaction)
- [Probabilistic machine learning](https://probml.github.io/pml-book/)
- [Advanced Machine Learning with Python](https://github.com/packtpublishing/advanced-machine-learning-with-python)
- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html)
- [Deep Learning](https://www.deeplearningbook.org/)
- [Deep Learning: A Visual Approach](https://www.glassner.com/portfolio/deep-learning-a-visual-approach/)
  - [notebook](https://github.com/blueberrymusic/Deep-Learning-A-Visual-Approach)
- [Pattern recognition and machine learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
- [Casual inference in statistics: a primer](http://bayes.cs.ucla.edu/PRIMER/)
- [Mathematics for Machine Learning](https://mml-book.github.io/)
  - [Matrix Calculus](https://www.matrixcalculus.org/)
- [Machine Learning - A First Course for Engineers and Scientists](https://smlbook.org/)
- [Machine Learning Refined: Foundations, Algorithms, and Applications 2e](https://github.com/jermwatt/machine_learning_refined)
- ---
- [Machine Learning From Scratch](https://github.com/eriklindernoren/ML-From-Scratch)
- [Machine Learning From Scratch: Derivations in Concept and Code](https://dafriedman97.github.io/mlbook/content/introduction.html)
- [Deep Learning From Scratch code](https://github.com/SethHWeidman/DLFS_code)
- [Machine Learning Notebooks, 3rd edition](https://github.com/ageron/handson-ml3)
- ---
- [Machine learning with Julia – How to Build and Deploy a Trained AI Model as a Web Service](https://www.freecodecamp.org/news/machine-learning-using-julia/)
- [Categories Great and Small](https://bartoszmilewski.com/2014/12/05/categories-great-and-small/)
  - [pdf](https://github.com/hmemcpy/milewski-ctfp-pdf/releases/tag/v1.3.0)
- ---
- [Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand ](https://github.com/nlpbook/nlpbook)
- [Practical Natural Language Processing](https://github.com/practical-nlp/practical-nlp-code)
- [Natural Language Processing with Transformers](https://github.com/nlp-with-transformers/notebooks)
- [Real-World Natural Language Processing](https://github.com/mhagiwara/realworldnlp)
- [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/)
- [Natural language processing](https://github.com/jacobeisenstein/gt-nlp-class/blob/master/notes/eisenstein-nlp-notes.pdf)
- ---
- [Artificial Intelligence: A Modern Approach 4e](http://aima.cs.berkeley.edu/)
## Classes
- *stanford*
  - [CS229: Machine Learning](https://cs229.stanford.edu/)
  - [CS230 Deep Learning](https://cs230.stanford.edu/)
  - [D2L Courses](https://c.d2l.ai/)
  - [DeepLearning.AI](https://www.deeplearning.ai/)
- *berkeley*
  - [CS 189/289A: Introduction to Machine Learning](https://eecs189.org/)
    - [spring 2023](https://people.eecs.berkeley.edu/~jrs/189/)
- *mit*
  - [6.036 Introduction to Machine Learning](https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019)
  - [6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/)
    - [source](https://github.com/aamini/introtodeeplearning)
  - [6.S898 Deep Learning](https://phillipi.github.io/6.s898/)
  - [6.5940 TinyML and Efficient Deep Learning Computing](https://efficientml.ai)
  - [MIT Deep Learning and Artificial Intelligence Lectures](https://deeplearning.mit.edu/)
- *princeton*
  - [COS 324 - Introduction to Machine Learning](https://princeton-introml.github.io/)
- *cornell*
  - [CS 4/5780: Intro to Machine Learning](https://www.cs.cornell.edu/courses/cs4780/2023fa/)
- *cmu*
  - *10-315: Introduction to Machine Learning*
    - [spring 2023](https://www.cs.cmu.edu/~10315-s23/)
    - [fall 2020](https://www.cs.cmu.edu/~aarti/Class/10315_Fall20/)
- *misc*
  - [Introduction To Machine Learning](https://home.cs.colorado.edu/~DrG/Courses/IntroToMachineLearning)
  - [CIS 4190/5190: Applied Machine Learning](https://www.seas.upenn.edu/~cis5190)
  - [Think Deep Learning](https://sunju.org/teach/DL-Spring-2020/)
  - [Scientific Machine Learning](https://sciml.tamids.tamu.edu/ecen-689-scientific-machine-learning-spr-2023/)
  - [Visualization Meets AI](https://web.cse.ohio-state.edu/~shen.94/5559/)
  - [Data Science: principles and practice](https://www.cl.cam.ac.uk/teaching/2021/DataSciII/)
  - [ECE 208/408 - The Art of Machine Learning](https://hajim.rochester.edu/ece/sites/zduan/teaching/ece408/index.html)
  - ---
  - [COMPSCI 589: Open Source ML Course](https://github.com/mlds-lab/COMPSCI-589)
  - [Machine Learning](https://www.keithdillon.com/index.php/teaching/)
  - [Deep learning](https://kenndanielso.github.io/mlrefined/index.html)
## Online resources
- [MIT Julialab](https://julia.mit.edu/)
- [Juliaup](https://github.com/JuliaLang/juliaup)
  - [Julia](https://julialang.org/)
    - [Julia packages](https://juliapackages.com/c/machine-learning)
    - [Top 9 Machine Learning Frameworks In Julia](https://analyticsindiamag.com/top-9-machine-learning-frameworks-in-julia/)
      - [juliaml](https://juliaml.github.io/)
      - [Fluxml](https://fluxml.ai/)
      - [ReinforcementLearning.jl](https://juliareinforcementlearning.org/)
- [Introduction to Deep Learning in Julia](https://www.analyticsvidhya.com/blog/2021/10/introduction-to-deep-learning-in-julia/)
  - [Deep Learning with Julia](https://deeplearningwithjulia.com/)
- [devdocs](https://devdocs.io/)
- [Allen NLP](https://github.com/allenai/allennlp)

## Research groups
- [Stanford Machine Learning Group](https://stanfordmlgroup.github.io/)
- [TinyML](https://tinyml.mit.edu/)
  - [papers and projects](https://github.com/gigwegbe/tinyml-papers-and-projects)
- [Princeton CS Artificial Intelligence & Machine Learning](https://aiml.cs.princeton.edu/)
- [Cornell Machine Learning](https://machinelearning.cis.cornell.edu/)
- [cmu machine learning department](https://www.ml.cmu.edu/)
- *AI research companies*
  - [OpenAI](https://openai.com/)
  - [Google AI](https://ai.google/)
    - [Google research](https://research.google/)
  - [IBM AI](https://research.ibm.com/artificial-intelligence)
  - [Microsoft AI](https://www.microsoft.com/en-us/research/research-area/artificial-intelligence/?)
  - [Meta](https://ai.meta.com/)
  - [nvidia](https://www.nvidia.com/en-us/ai-data-science/)

## Lists
- [Awesome Artificial Intelligence (AI)](https://project-awesome.org/owainlewis/awesome-artificial-intelligence)
  - [Awesome AI Awesomeness](https://github.com/amusi/awesome-ai-awesomeness)
  - [Awesome Generative AI](https://github.com/steven2358/awesome-generative-ai)
  - [Awesome Data Science](https://github.com/academic/awesome-datascience)
  - [Awesome Public Datasets](https://github.com/awesomedata/awesome-public-datasets)
- *competitions, datasets and models*
  - [OpenML](https://www.openml.org/)
  - [Open Neural Network Exchange](https://onnx.ai/)
  - [Kaggle](https://www.kaggle.com/)
  - [Hugging face](https://huggingface.co/)
  - [Model Zoo](https://modelzoo.co/)
  - [Model hub](http://modelhub.ai/)
  - *framework based*
    - [TensorFlow hub](https://www.tensorflow.org/hub)
    - [PyTorch hub](https://pytorch.org/hub/)
    - [Core ML](https://developer.apple.com/machine-learning/models/)
- *GUI tools*
  - [Orange data mining](https://orangedatamining.com/)
  - [Weka 3: Machine Learning Software in Java](https://www.cs.waikato.ac.nz/ml/weka/)
- [TOP 20 AI Open Source Projects in 2023](https://web3.career/learn-web3/top-ai-open-source-projects)

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