Machine Learning


ML Course: https://www.dhillon.ece.vt.edu/mlcourse.html



শূন্য থেকে পাইথন মেশিন লার্নিং : হাতেকলমে সাইকিট-লার্ন - হাতেকলমে মেশিন লার্নি সিরিজ, আইরিস ডেটাসেট প্রজেক্ট- রকিবুল হাসান

https://colab.research.google.com/github/raqueeb/ml-python/blob/master





https://inform.tmforum.org/ai-data-and-insights/2020/06/nwdaf-automating-the-5g-network-with-machine-learning-and-data-analytics/

https://github.com/chaoyanghe/Awesome-Federated-Learning

https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46614.pdf

https://mlc.committees.comsoc.org/research-library/

https://mlc.committees.comsoc.org/papers-with-code/

https://arxiv.org/pdf/1910.05054.pdf

https://arxiv.org/pdf/1907.08965.pdf

https://www.researchgate.net/publication/311222418_Learning_Radio_Resource_Management_in_5G_Networks_Framework_Opportunities_and_Challenges

http://cs229.stanford.edu/syllabus.html

https://liu.diva-portal.org/smash/get/diva2:1223862/FULLTEXT01.pdf

https://www.ttc.or.jp/application/files/8315/5305/6818/1_seminar20181126_FG_s.pdf

https://towardsdatascience.com/ml-notes-why-the-least-square-error-bf27fdd9a721

https://www.southampton.ac.uk/courses/modules/elec6253.page

https://paperswithcode.com

https://www.iith.ac.in/~asaidhiraj/ee5611_spring_2019.html

https://nptel.ac.in/courses/108/104/108104112/ : Optimization Math

https://elitedatascience.com/machine-learning-iteration#micro

https://aiwithr.github.io/

You can train different models in googles colab with the high-end machines: https://colab.research.google.com

https://aiwithr.github.io/

1. Machine Learning - Stanford - (Approx. 56 hours to complete). It is the best machine learning course. https://www.coursera.org/learn/machine-learning

2. মেশিন লার্নিং অ‍্যালগরিদম - নাফিস নিহাল- Well explained. Best and only book with theoretical knowledge. https://www.rokomari.com/book/173164/machine-learning-algorithm

3. শূন্য থেকে পাইথন মেশিন লার্নিং : হাতেকলমে সাইকিট-লার্ন - রকিবুল হাসান - Best for code. https://www.rokomari.com/book/174186/hatekolome-machine-learning--2nd-edition-


You can find datasets in here: https://www.kaggle.com/datasets

https://cloud.google.com/automl/

https://arxiv.org/pdf/1809.08707.pdf

http://www.netsciwis.com/ai-for-wireless

https://www.youtube.com/watch?v=thZ1MSumZX0

https://www.quantinsti.com/blog/top-10-machine-learning-algorithms-beginners?fbclid=IwAR1FRjsvOAscMsNv-DhRZUoS-OntlVmQA1beS1xPYuZWFJz38BiieDLw3jE

https://towardsdatascience.com/introduction-to-machine-learning-db7c668822c4

    • Linear/Logistic Regression, Bagging, Bayesian model, Neural Networks, Random forest, Gradient boosting, Hyperparameter optimization techniques etc.

    • Machine learning frameworks such as Keras, TensorFlow, Scikit-Learn, H2o, Spark etc.

    • data wrangling and data munging, using Big Data technologies

    • Programming (C++, Scala, Java, R, Python and/or C++)

https://raqueeb.gitbooks.io/mlbook-titanic/content/