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