https://github.com/ekramasif/basic-machine-learning

This is a repo of basic Machine Learning what I learn. More to go...
https://github.com/ekramasif/basic-machine-learning

ann artficial-neural-network artificial-intelligence bert-embeddings bert-model blstm collaborate data-science deep-learning embeddings keras lstm machine-learning natural-language-processing neural-network nlp pandas python seaborn tensorflow

Last synced: 10 days ago
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This is a repo of basic Machine Learning what I learn. More to go...

• Host: GitHub
• URL: https://github.com/ekramasif/basic-machine-learning
• Owner: ekramasif
• License: mit
• Created: 2021-08-19T19:02:46.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-10-17T07:57:45.000Z (20 days ago)
• Last Synced: 2024-10-19T10:35:25.075Z (18 days ago)
• Topics: ann, artficial-neural-network, artificial-intelligence, bert-embeddings, bert-model, blstm, collaborate, data-science, deep-learning, embeddings, keras, lstm, machine-learning, natural-language-processing, neural-network, nlp, pandas, python, seaborn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://nbviewer.org/github/ekramasif/Basic-Machine-Learning/tree/main/
• Size: 39.3 MB
• Stars: 79
• Watchers: 5
• Forks: 17
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        


Basic Machine Learning



[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/ekramasif/Basic-Machine-Learning/blob/main/LICENSE)



    



> - This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources.

## Contents

- [Introduction](#general)

- [Interview Resources](#interview)

- [Artificial Intelligence](#ai)

- [Genetic Algorithms](#ga)

- [Resources on Kaggle](#kaggle)

- [Cheat Sheets](#cs)

- [Classification](#classification)

- [Linear Regression](#linear)

- [Logistic Regression](#logistic)

- [Model Validation using Resampling](#validation)

    - [Cross Validation](#cross)

- [Deep Learning](#deep)

    - [Frameworks](#frame)

    - [Feed Forward Networks](#feed)

    - [Recurrent Neural Nets, LSTM, GRU](#rnn)

    - [Restricted Boltzmann Machine, DBNs](#rbm)

    - [Autoencoders](#auto)

    - [Convolutional Neural Nets](#cnn)

    - [Graph Representation Learning](#nrl)

- [Natural Language Processing](#nlp)

    - [Word2Vec](#word2vec)

- [Computer Vision](#vision)

- [Support Vector Machine](#svm)

- [Reinforcement Learning](#rl)

- [Decision Trees](#dt)

- [Random Forest / Bagging](#rf)

- [Boosting](#gbm)

- [Bayesian Machine Learning](#bayes)

- [Semi Supervised Learning](#semi)

- [Other Useful Tutorials](#other)



## Introduction

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

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Contributing

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Have anything in mind that you think is awesome and would fit in this list? Feel free to send me a pull request!