# Machine Learning Algorithms

- [10 types of regressions. Which one to use?](https://www.linkedin.com/posts/isaacbaum_10-types-of-regressions-which-one-to-use-activity-6610217740546428929-EHnH)
- [How To Implement Machine Learning Algorithm Performance Metrics From Scratch With Python](https://www.linkedin.com/posts/data-science-central_how-to-implement-machine-learning-algorithm-activity-6611815688283709440-1GJ2)
- [ML Algos with interactive Jupyter Demos](https://lnkd.in/fZq3yW6)
- [Barebone implementation of ML models, algorithms](https://lnkd.in/fpapv7x)
- [Jackknife and linear regression in Excel: implementation and comparison](https://www.datasciencecentral.com/profiles/blogs/comparing-linear-regression-with-the-jackknife-method)
- [Logistic Regression Vs Decision Trees Vs SVM: Part I](https://www.linkedin.com/posts/data-science-central_logistic-regression-vs-decision-trees-vs-activity-6623758378239881216-I8gQ)
- XGBoost: [Part 1, XGBoost trees for Regression](https://www.linkedin.com/posts/joshua-starmer-95a554130_xgboost-part1-xgboost-trees-for-regression-activity-6612344313353433088-01sJ) | [Part 2, XGBoost trees for Classification](https://www.linkedin.com/posts/joshua-starmer-95a554130_xgboost-part-2-xgboost-trees-for-classification-activity-6622492333839577088-SCUT)
- [Curve Fitting Methods #datascience](https://www.linkedin.com/posts/asif-bhat_datasciencepost-on-instagram-curve-fitting-activity-6610166201073131520-Pzls)
- [Main Formula on Machine Learning](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelerning-artificialintellegence-activity-6623589978637205504-inka)
- [Machine Learning Algorithm Map](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintellegence-activity-6618736049176842240-Jdf_)
- [The world of machine learning algorithm](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintellegence-activity-6618868890162298880-Bxjn)
- [New State of the Art AI Optimizer: Rectified Adam (RAdam)](https://www.linkedin.com/posts/montrealai_machinelearning-tensorflow-pytorch-activity-6622133450843443200-GN1v)
- [27 Great Resources About Logistic Regression](https://www.linkedin.com/posts/data-science-central_27-great-resources-about-logistic-regression-activity-6622611841715625984-DYHS)
- [Designing better algorithms: 5 case studies](https://www.linkedin.com/posts/data-science-central_designing-better-algorithms-5-case-studies-activity-6607662317066944512-jD_6)
- [R and Python Code for some popular machine learning algorithm](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6624922591301861376-oN0o)
- [Top Machine Learning Algorithm for Preditions](https://www.linkedin.com/posts/nabihbawazir_top-machine-learning-algorithm-for-preditions-activity-6603862704757272576-ULbe)
- [Sigmoid Neuron Learning Algorithm Explained With Math](https://towardsdatascience.com/sigmoid-neuron-learning-algorithm-explained-with-math-eb9280e53f07)
- [Linear regression in Python: Use of numpy, scipy, and statsmodels](https://www.linkedin.com/posts/data-science-central_httpswwwdatasciencecentralcomprofiles-activity-6607284839810945024-hxnF)
- [Smooth markets: A basic mechanism for organizing gradient-based learners Balduzzi et al](https://lnkd.in/e-PrH45)
- [27 Great Articles About Machine Learning Algorithms](https://www.linkedin.com/posts/vincentg_27-great-articles-about-machine-learning-activity-6634648891654492161-c632)
- [Alternatives to the Gradient Descent Algorithm](https://www.linkedin.com/posts/data-science-central_alternatives-to-the-gradient-descent-algorithm-activity-6627445954511458304-5j1V)
- [A Tour of Machine Learning Algorithms](https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/)
- [Shortest Path Algorithms, Part 2: Floyd�Warshall's And A-Star](https://www.linkedin.com/posts/vincentg_key-graph-based-shortest-path-algorithms-activity-6647272320631128064-KXb4)
- [Journey to Machine Learning – Support Vector Machines](https://www.linkedin.com/posts/vipulppatel_data-analytics-businessintelligence-activity-6632121501585809408-knOP)
- [Predicting AirBnB prices in Lisbon: Trees and Random Forest](https://www.linkedin.com/posts/towards-data-science_predicting-airbnb-prices-in-lisbon-trees-activity-6647284148094545920-w5YF)
- [10 types of regressions. Which one to use?](https://www.linkedin.com/posts/data-science-central_10-types-of-regressions-which-one-to-use-activity-6641726778312314882-xY-N)
- [Backpropagation and Labeled data](https://www.linkedin.com/posts/deeplearningai_backpropagation-and-labeled-data-are-the-activity-6626194119683305472-c-ON)
- [140 ML Formulas](https://www.linkedin.com/posts/bo-li-8503b896_140-machine-learning-formulas-activity-6632888211825881088-8q_V)
- [Sparse Forest with FIL](https://www.linkedin.com/posts/miguelusque_sparse-forests-with-fil-activity-6628874338785337344-89hf)
- [Google T5 Explores the Limits of Transfer Learning](https://www.linkedin.com/posts/eric-feuilleaubois-ph-d-43ab0925_google-t5-explores-the-limits-of-transfer-activity-6631788661920804864-wH9d)
- [🉐 𝘈𝘋𝘝𝘈𝘕𝘛𝘈𝘎𝘌𝘚 𝘈𝘕𝘋 🉐𝘗𝘐𝘛𝘍𝘈𝘓𝘓𝘚 𝘖𝘍 𝘋𝘐𝘍𝘍𝘌𝘙𝘌𝘕𝘛 𝘈𝘓𝘎𝘖𝘙𝘐𝘛𝘏𝘔𝘚](https://www.linkedin.com/posts/ashishpatel2604_datascience-deeplearning-machinelearning-activity-6668792791658852352-87Po)
- [One-Class Classification Algorithms for Imbalanced Datasets](https://machinelearningmastery.com/one-class-classification-algorithms/)
- [XGboost related discussion](https://twitter.com/a_erdem4/status/1265176369731944448)
- [Estimate the Number of Experiment Repeats for Stochastic Machine Learning Algorithms](https://machinelearningmastery.com/estimate-number-experiment-repeats-stochastic-machine-learning-algorithms/)
- [Top Machine Learning Algorithm](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintellegence-activity-6656808477152907264-JAwa)
- [14 Different Types of Learning in Machine Learning](https://machinelearningmastery.com/types-of-learning-in-machine-learning/)
- [6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)](https://twitter.com/analyticbridge/status/1261772163289354241)
- [Intuitively, How Can We (Better) Understand Logistic Regression by Angela Shi](https://www.linkedin.com/posts/towards-data-science_intuitively-how-can-we-better-understand-activity-6660097072131379200-m7s3)
- [How to Develop Multi-Output Regression Models with Python](https://machinelearningmastery.com/multi-output-regression-models-with-python/)
- [A Gentle Introduction to Cross-Entropy for Machine Learning](https://machinelearningmastery.com/cross-entropy-for-machine-learning/)
- [How to Calculate Correlation Between Variables in Python](https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/)
- [TabNet : Attentive Interpretable Tabular Learning](https://github.com/dreamquark-ai/tabnet)
- [TabNet](https://cloud.google.com/blog/products/ai-machine-learning/ml-model-tabnet-is-easy-to-use-on-cloud-ai-platform)
- [CascadTabNet is an automatic table recognition method for interpretation of tabular data in document images and extract them](https://www.linkedin.com/posts/philipvollet_datascience-network-analytics-activity-6701574164647690240-ANk3)
[Better than most LR's out there (Pytorch based)](https://github.com/dreamquark-ai/tabnet)
- [An overview of gradient descent optimization algorithms](https://ruder.io/optimizing-gradient-descent/)

# Contributing

Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the [CONTRIBUTING](../CONTRIBUTING.md) guidelines, also have a read about our [licensing](../LICENSE.md) policy.

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