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https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#1-linear-regression
Linear Regression, Clearly Exlplained!!! by StatQuesthttps://www.youtube.com/watch?v=nk2CQITm_eo&ab_channel=StatQuestwithJoshStarmer
Linear Regression by Jim Frosthttps://statisticsbyjim.com/regression/linear-regression/
7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regressionhttps://statisticsbyjim.com/regression/ols-linear-regression-assumptions/
Gauss-Markov Theoremhttps://statisticsbyjim.com/regression/gauss-markov-theorem-ols-blue/
Linear Regression β€” Detailed Viewhttps://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86
Building Linear Regression (Least Squares) with Linear Algebrahttps://towardsdatascience.com/building-linear-regression-least-squares-with-linear-algebra-2adf071dd5dd
Linear Regression using Gradient Descenthttps://towardsdatascience.com/linear-regression-using-gradient-descent-97a6c8700931
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#2-regularization
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When will L1 regularization work better than L2 and vice versa?https://stats.stackexchange.com/questions/184019/when-will-l1-regularization-work-better-than-l2-and-vice-versa
What is the difference between L1 and L2 regularization? How does it solve the problem of overfitting? Which regularizer to use and when?https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization-How-does-it-solve-the-problem-of-overfitting-Which-regularizer-to-use-and-when
What is elastic net regularization, and how does it solve the drawbacks of Ridge (𝐿2 ) and Lasso (𝐿1 )?https://stats.stackexchange.com/questions/184029/what-is-elastic-net-regularization-and-how-does-it-solve-the-drawbacks-of-ridge
Ridge, LASSO, and ElasticNet Regressionhttps://towardsdatascience.com/ridge-lasso-and-elasticnet-regression-b1f9c00ea3a3
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#3-logistic-regression
The Intuitive Explanation of Logistic Regressionhttps://towardsdatascience.com/the-intuitive-explanation-of-logistic-regression-a0375b1bee54
StatQuest: Logistic Regressionhttps://www.youtube.com/watch?v=yIYKR4sgzI8&ab_channel=StatQuestwithJoshStarmer
Logistic Regression by Andrew Nghttps://www.youtube.com/watch?v=-la3q9d7AKQ&ab_channel=ArtificialIntelligence-AllinOne
Logistic Regression by Amherst Collegehttps://nhorton.people.amherst.edu/ips9/IPS_09_Ch14.pdf
Intuition behind Log-loss scorehttps://towardsdatascience.com/intuition-behind-log-loss-score-4e0c9979680a
Log Loss Function by Alex Dyakonovhttps://dasha.ai/en-us/blog/log-loss-function
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#4-gradient-descent
Gradient Descent From Scratch by Analytics Vidhyahttps://www.analyticsvidhya.com/blog/2021/05/gradient-descent-from-scratch-complete-intuition/#:~:text=The%20intuition%20behind%20Gradient%20Descent&text=We%20have%20to%20find%20the,between%20actual%20and%20predicted%20values.
Gradient descent, how neural networks learnhttps://www.youtube.com/watch?v=IHZwWFHWa-w&ab_channel=3Blue1Brown
Stochastic Gradient Descent, Clearly Explained!!! by Josh Starmerhttps://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer
Gradient Descent Intuition β€” How Machines Learnhttps://medium.com/x8-the-ai-community/gradient-descent-intuition-how-machines-learn-d29ad7464453
The Math and Intuition Behind Gradient Descent by Suraj Bansalhttps://medium.datadriveninvestor.com/the-math-and-intuition-behind-gradient-descent-13c45f367a11
Batch gradient descent versus stochastic gradient descenthttps://stats.stackexchange.com/questions/49528/batch-gradient-descent-versus-stochastic-gradient-descent
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#5-decision-tree
Decision Trees Explained by James Thornhttps://towardsdatascience.com/decision-trees-explained-3ec41632ceb6
A Guide to Decision Trees for Beginnershttps://www.kaggle.com/code/vipulgandhi/a-guide-to-decision-trees-for-beginners
Decision and Classification Trees, Clearly Explained!!! by Josh Starmerhttps://www.youtube.com/watch?v=_L39rN6gz7Y&ab_channel=StatQuestwithJoshStarmer
Information Gain and Mutual Information for Machine Learning by Jason Brownleehttps://machinelearningmastery.com/information-gain-and-mutual-information/#:~:text=Mutual%20Information%20Related%3F-,What%20Is%20Information%20Gain%3F,samples%2C%20and%20hence%20less%20surprise.
A Simple Explanation of Information Gain and Entropy by Victor Zhouhttps://victorzhou.com/blog/information-gain/
How to program a decision tree in Python from 0https://anderfernandez.com/en/blog/code-decision-tree-python-from-scratch/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#6-random-forest
Building Intuition for Random Forests by Rishi Sidhuhttps://medium.com/x8-the-ai-community/building-intuition-for-random-forests-76d36fa28c5e
An Introduction to Random Forest Algorithm for beginnershttps://www.analyticsvidhya.com/blog/2021/10/an-introduction-to-random-forest-algorithm-for-beginners/
Feature Importance in Random Foresthttps://mljar.com/blog/feature-importance-in-random-forest/
Detailed Explanation of Random Forests Features importance Biashttps://medium.com/@eng.mohammed.saad.18/detailed-explanation-of-random-forests-features-importance-bias-8755d26ac3bc
Random Forest: A Complete Guide for Machine Learning by Niklas Dongeshttps://builtin.com/data-science/random-forest-algorithm
Random Forest Simple Explanation by Will Koehrsenhttps://williamkoehrsen.medium.com/random-forest-simple-explanation-377895a60d2d
Why Choose Random Forest and Not Decision Treeshttps://towardsai.net/p/machine-learning/why-choose-random-forest-and-not-decision-trees
When to use Random Foresthttps://datascience.stackexchange.com/questions/54751/when-to-use-random-forest
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#7-gradient-boosted-trees
The Intuition Behind Gradient Boosting & XGBoost by Bobby Tanhttps://towardsdatascience.com/the-intuition-behind-gradient-boosting-xgboost-6d5eac844920
Gradient Boosting Algorithm: A Complete Guide for Beginnershttps://www.analyticsvidhya.com/blog/2021/09/gradient-boosting-algorithm-a-complete-guide-for-beginners/
Gradient Boosting Trees vs. Random Forestshttps://www.baeldung.com/cs/gradient-boosting-trees-vs-random-forests#:~:text=4.3.-,Advantages%20and%20Disadvantages,and%20start%20modeling%20the%20noise.
Gradient Boosting In Classification: Not a Black Box Anymore!https://blog.paperspace.com/gradient-boosting-for-classification/
A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learninghttps://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#8-xgboost
XGBoost Paperhttps://arxiv.org/abs/1603.02754
A Gentle Introduction to XGBoost for Applied Machine Learninghttps://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/
XGBoost A Scalable Tree Boosting System by Tianqi Chenhttps://www.youtube.com/watch?v=Vly8xGnNiWs&ab_channel=RealDataScienceUSA%28formerlyDataScience.LA%29
CatBoost vs. LightGBM vs. XGBoosthttps://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924
XGBoost, LightGBM or CatBoost β€” which boosting algorithm should I use?https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#9-k-nearest-neighborsknn
KNN algorithm: Introduction to K-Nearest Neighbors Algorithm for Regressionhttps://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/
K-Nearest Neighbors πŸ‘¨β€πŸ‘©β€πŸ‘§β€πŸ‘¦https://www.romaglushko.com/blog/k-nearest-neighbors/
Pros And Cons Of The K-Nearest Neighbors (KNN) Algorithmhttps://roboticsbiz.com/pros-and-cons-of-the-k-nearest-neighbors-knn-algorithm/
StatQuest: K-nearest neighbors, Clearly Explainedhttps://www.youtube.com/watch?v=HVXime0nQeI&ab_channel=StatQuestwithJoshStarmer
The KNN Algorithm – Explanation, Opportunities, Limitationshttps://neptune.ai/blog/knn-algorithm-explanation-opportunities-limitations#:~:text=KNN%20is%20most%20useful%20when,of%20desired%20precision%20and%20accuracy.
K-Nearest Neighbors (KNN) Classification with scikit-learnhttps://www.datacamp.com/tutorial/k-nearest-neighbor-classification-scikit-learn
Develop k-Nearest Neighbors in Python From Scratchhttps://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#10-k-means-clustering
Elbow Method for Finding the Optimal Number of Clusters in K-Meanshttps://www.analyticsvidhya.com/blog/2021/01/in-depth-intuition-of-k-means-clustering-algorithm-in-machine-learning/
Intuition Behind K-Meanshttps://pianalytix.com/intuition-behind-k-means/
k-Means Advantages and Disadvantageshttps://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages
Difference between K means and Hierarchical Clusteringhttps://www.geeksforgeeks.org/difference-between-k-means-and-hierarchical-clustering/
Learn K-Means and Hierarchical Clustering Algorithms in 15 minuteshttps://medium.com/sfu-cspmp/learn-k-means-and-hierarchical-clustering-algorithms-in-15-minute-221661bbec9e
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#11-hierarchical-clustering
Hierarchical clustering explainedhttps://towardsdatascience.com/hierarchical-clustering-explained-e59b13846da8
HOW THE HIERARCHICAL CLUSTERING ALGORITHM WORKShttps://dataaspirant.com/hierarchical-clustering-algorithm/
How to understand the drawbacks of Hierarchical Clustering?https://stats.stackexchange.com/questions/183873/how-to-understand-the-drawbacks-of-hierarchical-clustering
Choosing the right linkage method for hierarchical clusteringhttps://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
Agglomerative Hierarchical Clusteringhttps://online.stat.psu.edu/stat505/lesson/14/14.4
Lecture 3: Hierarchical Methodshttps://cse.buffalo.edu/~jing/cse601/fa12/materials/clustering_hierarchical.pdf
Hierarchical Clustering in Pythonhttps://blog.quantinsti.com/hierarchical-clustering-python/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#12-support-vector-machine
Support Vector Machines: An Intuitive Approachhttps://www.kdnuggets.com/2022/08/support-vector-machines-intuitive-approach.html
Support Vector Machine(SVM): A Complete guide for beginnershttps://www.analyticsvidhya.com/blog/2021/10/support-vector-machinessvm-a-complete-guide-for-beginners/
Deep Dive into Support Vector Machinehttps://towardsdatascience.com/deep-dive-into-support-vector-machine-654c8d517103
Support Vector Machines Part 1 (of 3): Main Ideas!!! by Josh Starmerhttps://www.youtube.com/watch?v=efR1C6CvhmE&ab_channel=StatQuestwithJoshStarmer
SVM and Kernel SVMhttps://towardsdatascience.com/svm-and-kernel-svm-fed02bef1200
Kernel Functions-Introduction to SVM Kernel & Exampleshttps://data-flair.training/blogs/svm-kernel-functions/
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#13-artificial-neural-nets
Deep Learning vs. Classical MLhttps://towardsdatascience.com/deep-learning-vs-classical-machine-learning-9a42c6d48aa
Backpropagationhttps://brilliant.org/wiki/backpropagation/
Neural Networks by Analogy with Linear Regressionhttps://joshuagoings.com/2020/05/05/neural-network/
Neural Networks and Deep Learninghttp://neuralnetworksanddeeplearning.com/
Colah's Bloghttp://colah.github.io/
CNN's for Deep Learninghttps://python.plainenglish.io/convolution-neural-network-cnn-in-deep-learning-77f5ab457166
https://patch-diff.githubusercontent.com/PlayingNumbers/ML_Algorithms_Course#14-collaborative-filtering
Non-negative matrix factorization for recommendation systemshttps://medium.com/logicai/non-negative-matrix-factorization-for-recommendation-systems-985ca8d5c16c
Collaborative Filtering Example - Googlehttps://developers.google.com/machine-learning/recommendation/collaborative/basics
Scikit Learn Decompositionhttps://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/decomposition/nmf.py#L1235
Quick Intro Nonnegative Matrix Factorizationhttps://heather.cs.ucdavis.edu/NMFTutorial.pdf
Algorithms for Non-Negative Matrix Factorizationhttps://proceedings.neurips.cc/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf
Optimal number of latent factors in non-negative matrix factorization?https://stats.stackexchange.com/questions/111205/how-to-choose-an-optimal-number-of-latent-factors-in-non-negative-matrix-factori
How to Use Cross-Validation for Matrix Completionhttps://towardsdatascience.com/how-to-use-cross-validation-for-matrix-completion-2b14103d2c4c
Matrix Factorization for Movie Recommendations in Pythonhttps://beckernick.github.io/matrix-factorization-recommender/
NMF β€” A visual explainer and Python Implementationhttps://towardsdatascience.com/nmf-a-visual-explainer-and-python-implementation-7ecdd73491f8
Recommendation System Series Part 4: The 7 Variants of Matrix Factorization For Collaborative Filteringhttps://towardsdatascience.com/recsys-series-part-4-the-7-variants-of-matrix-factorization-for-collaborative-filtering-368754e4fab5
Collaborative Filtering: Matrix Factorization Recommender Systemhttps://www.jiristodulka.com/post/recsys_cf/
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