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Linear Regression
- A BEGINNERS GUIDE TO REGRESSION TECHNIQUES
- Linear Regression Algorithm | Linear Regression in Python | Machine Learning Algorithm | Edureka - YouTube
- In Depth: Linear Regression | Python Data Science Handbook
- Linear Models - YouTube
- Statistics 101: Linear Regression, The Very Basics - YouTube
- How to Implement Linear Regression From Scratch in Python
- Linear Regression using Python - Towards Data Science
- Mathematical explanation for Linear Regression working - GeeksforGeeks
- Gradient Descent in Linear Regression - GeeksforGeeks
- ML | Normal Equation in Linear Regression - GeeksforGeeks
- Univariate Linear Regression in Python - GeeksforGeeks
- How to do Linear Regression and Logistic Regression in Machine Learning?
- Linear Regression (Python Implementation) - GeeksforGeeks
- ML | Multiple Linear Regression using Python - GeeksforGeeks
- Python | Implementation of Polynomial Regression - GeeksforGeeks
- Simple Linear Regression From Scratch in Numpy - Towards Data Science
- A Complete Tutorial on Ridge and Lasso Regression in Python
- Python/linear_regression.py at master · TheAlgorithms/Python
- Python | Linear Regression using sklearn - GeeksforGeeks
- ML | Locally weighted Linear Regression - GeeksforGeeks
- Statistics PL15 - Multiple Regression - YouTube
- Statistics PL18 - Nonlinear Regression - YouTube
- Isotonic Regression is THE Coolest Machine-Learning Model You Might Not Have Heard Of
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Logistic Regression
- Logistic Regression - YouTube
- TLM | Logistic Regression
- Logistic regression - Wikipedia
- Maximum likelihood and gradient descent demonstration – Zlatan Kremonic
- An Introduction to Logistic Regression - Towards Data Science
- A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation
- Logistic model - Maximum likelihood
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SVD
- Gilbert strang - SVD
- You Don’t Know SVD (Singular Value Decomposition)
- (114) A geometrical interpretation of the SVD - YouTube
- SVD playlist
- Gilbert strang - Computing Eigenvalues and Singular Values
- Gilbert strang - Singular Value Decomposition
- Computing the SVD
- Lecture 47 — Singular Value Decomposition | Stanford University
- How to Calculate the Singular-Value Decomposition (SVD) from Scratch with Python
- What is an intuitive explanation of singular value decomposition (SVD)? - Quora
- What is the meaning behind the singular value in Singular Value Decomposition? - Quora
- What is the best way of introducing singular value decomposition (SVD) on a linear algebra course? Why is it so important? Are there any applications which have a real impact? - Quora
- What's the difference between SVD and SVD++? - Quora
- What is the purpose of Singular Value Decomposition? - Quora
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Covariance
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PCA
- Implementing a Principal Component Analysis (PCA)– in Python, step by step
- The Mathematics Behind Principal Component Analysis
- A tutorial on Principal Components Analysis
- Principal Component Analysis - Youtube
- Dimensionality Reduction For Dummies — Part 1: Intuition
- Data Analysis 6: Principal Component Analysis (PCA) - Computerphile
- Visual Explanation of Principal Component Analysis, Covariance, SVD
- luis serrano pca
- StatQuest: Principal Component Analysis (PCA), Step-by-Step - YouTube
- StatQuest: PCA in Python - YouTube
- StatQuest: PCA - Practical Tips
- Dimensionality reduction and PCA
- Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)
- Visualizing Classifier Boundaries Using Kernel PCA
- Understanding Principal Component Analysis Once And For All
- How to Calculate Principal Component Analysis (PCA) from Scratch in Python
- Implementing a Principal Component Analysis (PCA)
- What is an intuitive explanation for PCA? - Quora
- What is an intuitive explanation of the relation between PCA and SVD? - Quora
- Why don't people use SVD in PCA rather than eigen value decomposition? - Quora
- (19) Statistics PL03 - Descriptive Statistics II - YouTube
- Pca
- Dimensionality Reduction and Principal Component Analysis (PCA) Explained
- In Depth: Principal Component Analysis
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LDA
- Linear Discriminant Analysis – Bit by Bit
- Linear Discriminant Analysis
- Linear Discriminant Analysis
- A Geometric Intuition for Linear Discriminant Analysis
- Machine Learning with Python: Linear Discriminant Analysis in Python
- Using Linear Discriminant Analysis (LDA) for data Explore: Step by Step. | Blog
- Linear Discriminant Analysis (LDA) Numerical Example
- Classification — Linear Discriminant Analysis
- Linear Discriminant Analysis
- Feature Reduction using — PCA & LDA
- Machine Learning: In-Depth LDA (Linear Discriminant Analysis) Python Example On The Iris Dataset. - YouTube
- How to implement Linear Discriminant Analysis python | +91-7307399944 for query - YouTube
- Linear Discriminant Analysis In Python - Towards Data Science
- Implementing LDA in Python with Scikit-Learn
- Machine Learning with Python: Linear Discriminant Analysis in Python
- (Linear Discriminant Analysis) using Python - Journey 2 Artificial Intelligence - Medium
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SVM
- Please explain Support Vector Machines (SVM) like I am a 5 year old. : MachineLearning
- Statquest - Support Vector Machines, Clearly Explained!!!
- Coursera Support vector machine
- Intuition for the Support Vector Machine (primal form)
- Chapter 2 : SVM (Support Vector Machine) Theory - Machine Learning 101 - Medium
- Understanding Support Vector Machines algorithm (along with code)
- Support Vector Machines in Scikit-learn (article) - DataCamp
- Implementing SVM and Kernel SVM with Python Scikit-Learn
- Support Vector Machines for Machine Learning
- cs229-notes3.dvi
- Classifying data using Support Vector Machines(SVMs) in Python - GeeksforGeeks
- Support Vector Machine (SVM) - Fun and Easy Machine Learning
- In-Depth: Support Vector Machines | Python Data Science Handbook
- Sentdex - Support Vector Machine Intro and Application - YouTube
- Support Vector Machine — Introduction to Machine Learning Algorithms
- Classification From Scratch, Part 7 of 8: SVM - DZone Big Data
- Chapter 3.1 : SVM from Scratch in Python. - Deep Math Machine learning.ai - Medium
- adityajn105/SVM-From-Scratch: An Implementation of SVM - Support Vector Machines using Linear Kernel
- SVM-From-Scratch/Support Vector Machine From Scratch.ipynb at master · adityajn105/SVM-From-Scratch
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Decision Trees
- StatQuest: Decision Trees
- Decision Tree Algorithm | Decision Tree in Python | Machine Learning Algorithms | Edureka
- Classification And Regression Trees for Machine Learning
- How To Implement The Decision Tree Algorithm From Scratch In Python
- Clas - 5 Data Science Training | Decision Tree Classifier Explained | Edureka
- Understanding Decision Trees for Classification in Python
- A Simple Explanation of Information Gain and Entropy
- A Simple Explanation of Gini Impurity
- In-Depth: Decision Trees and Random Forests
- The Simple Math behind 3 Decision Tree Splitting criterions
Entropy sum
- Decision tree Learning example | ID3 |
- (137) Decision Tree Classification Algorithm – Solved Numerical Question 2 in Hindi - YouTube
- ID3
- Decision Trees for Classification: A Machine Learning Algorithm | Xoriant Blog
GINI sum
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Feature Selection
- How do I select features for Machine Learning? - YouTube
- Feature Selection in Machine Learning using Python - YouTube
- Hands-on with Feature Selection Techniques: An Introduction
- How to Choose a Feature Selection Method For Machine Learning
- Feature Selection For Machine Learning in Python
- FEATURE SELECTION Techniques for Classification Models
- 1.13. Feature selection — scikit-learn 0.22.2 documentation
- anujdutt9/Feature-Selection-for-Machine-Learning: Methods with examples for Feature Selection during Pre-processing in Machine Learning.
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Evaluation metrics
- MLM- A Gentle Introduction to Model Selection for Machine Learning
- Data Preprocessing : Concepts
- About Feature Scaling and Normalization
- Measuring Search Effectiveness
- Classifier evaluation with imbalanced datasets
- ROC
- ROC curve 101
- A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores
- Multi-Class Metrics Made Simple, Part II: the F1-score
- Feature Scaling with scikit-learn
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Bagging and boosting
- Ensemble methods: bagging, boosting and stacking
- UDACITY: Bootstrap aggregating bagging
- Bagging, boosting and stacking in machine learning
- A Gentle Introduction to the Bootstrap Method
- Ensemble methods: bagging, boosting and stacking
- Basics of Ensemble Learning Explained in Simple English
- A Comprehensive Guide to Ensemble Learning (with Python codes)
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating
- Random Forests for Complete Beginners
- Random Forest(Bootstrap Aggregation) Easily Explained
- Selecting good features – Part III: random forests
- Effect of Irrelevant Features
- TheAlgorithms-random_forest_classification
- UDACITY: Boosting
- AdaBoost, Clearly Explained
- Boosting and AdaBoost for Machine Learning
- A Kaggle Master Explains Gradient Boosting
- A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
- Gradient Boost Part 1: Regression Main Ideas
- Gradient boosting
- Gradient Boosting for Linear Regression - why does it not work?
- Boosting Machine Learning Tutorial | Adaptive Boosting, Gradient Boosting, XGBoost | Edureka
- Xgboost (Boosting) Intuition Easily Explained
- An End-to-End Guide to Understand the Math behind XGBoost
- How to Develop Your First XGBoost Model in Python with scikit-learn
- Why is the boosting algorithm robust to overfitting?
- 30 Questions to test a data scientist on Tree Based Models
- 45 questions to test Data Scientists on Tree Based Algorithms (Decision tree, Random Forests, XGBoost)
- 40 Questions to ask a Data Scientist on Ensemble Modeling Techniques (Skilltest Solution)
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Graphical Models, Bayesian networks
- A Sober Look at Bayesian Neural Networks
- A Gentle Introduction to Bayesian Belief Networks
- Graphical Models
- Directed Graphical Models
- Introduction to Bayesian Networks
- Probabilistic Graphical Models
- Introduction to Bayesian Networks | Implement Bayesian Networks In Python | Edureka
- Bayesian Networks - YouTube
- (24) Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning] - YouTube
- A friendly introduction to Bayes Theorem and Hidden Markov Models - YouTube(24) belief network - YouTube
- (24) Bayesian Belief Network Explained with Solved Example in Hindi - YouTube
- (24) BayesianNetworks - YouTube
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Clustering
- The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need
- Beginner’s Guide To K-Means Clustering
- K-means Clustering Algorithm: Know How It Works
- In Depth: k-Means Clustering
- TheAlgorithms - k_means_clust
- K means Clustering – Introduction
- Image compression using K-means clustering
- Introduction to Image Segmentation with K-Means clustering
- ML | Mini Batch K-means clustering algorithm
- Clustering in Machine Learning
- Different Types of Clustering Algorithm
- In Depth: Gaussian Mixture Models
- In-Depth: Kernel Density Estimation
- A Beginner’s Guide to Hierarchical Clustering and how to Perform it in Python
- ML | Hierarchical clustering (Agglomerative and Divisive clustering)
- Hierarchical Clustering / Dendrogram: Simple Definition, Examples
- Hierarchical Clustering / Dendrograms
- ML | Mean-Shift Clustering
- ML | Spectral Clustering
- ML | Fuzzy Clustering
- DBSCAN Clustering in ML | Density based clustering
- How DBSCAN works and why should we use it?
- What is the Jaccard Index?
- K-Means Clustering Implementation in Python | Kaggle
- Python Programming Tutorials
- Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering
- K-Means Clustering in Python - Blog by Mubaris NK
- Implementing K Means Clustering from Scratch - in Python - The Nadig Blog
- Machine Learning Workflows in Python from Scratch Part 1: Data Preparation
- Wikipedia - Single linkage Clustering
- Pyclustering
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Naive bayes
- Luis Serrano - Naive Bayes classifier: A friendly approach
- Andrew Ng Naive Bayes Generative Learning Algorithms
- Andrew Ng Naive Bayes Text Clasification
- 3blue1brown- Bayes theorem, and making probability intuitive
- 3blue1brown- The quick proof of Bayes' theorem
- Brandon Rohrer - How Bayes Theorem works
- Naive Bayes - Georgia Tech - Machine Learning
- The Bayesian Trap
- In Depth: Naive Bayes Classification
- Naive Bayes Classifier in Python | Naive Bayes Algorithm | Machine Learning Algorithm | Edureka
- How to Develop a Naive Bayes Classifier from Scratch in Python
- Naive Bayes Classifier From Scratch
- kaggle - Naive Bayes Classifier¶
- Naive Bayes Classifiers
- Understanding Naive Bayes Classifier from scratch : Python code
- kDnuggets - Naive Bayes from Scratch using Python only – No Fancy Frameworks
- Naïve Bayes for Machine Learning – From Zero to Hero
- How Bayes’ Theorem is Applied in Machine Learning
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MLE, MAP, Mixture models, expectation maximization
- The Only Theorem Data Scientists Need To Know
- Pieter abeel Maximum Likelihood Examples\
- Gaussians.pdf
- normal distribution - Maximum Likelihood Estimators - Multivariate Gaussian - Cross Validated
- (9) Maximum Likelihood estimation - an introduction part 1 - YouTube
- Maximum Likelihood Estimation Explained - Normal Distribution
- Probability concepts explained: Maximum likelihood estimation
- Maximum likelihood estimation of normal distribution - Daijiang Li
- cs229-notes2.pdf
- An Introductory Guide to Maximum Likelihood Estimation (with a case study in R)
- YOUTUBE - MLE
- Gaussian Mixture Model - GeeksforGeeks
- 5.pdf
- Gaussian Mixture Models Clustering Algorithm Explained
- analytics vidya GMM
- Hidden markov model-GMM-tf2
- MLM-A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
- MLM-A Gentle Introduction to Monte Carlo Sampling for Probability
- Gaussian Mixture Model clustering: how to select the number of components (clusters)
- A Novel Ship Detector Based on Gaussian Mixture Model and K-Means Algorithm | SpringerLink
- Probability concepts explained: Bayesian inference for parameter estimation.
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EXPECTATION MAXIMIZATION
- MLM-A Gentle Introduction to Expectation-Maximization (EM Algorithm)
- Edureka expectation maximization
- YOUTUBE - EXPECTATION MAXIMIZATION
- YOUTUBE-EM
- Gaussian Mixture Model - GeeksforGeeks
- Gaussian Mixture Models Clustering Algorithm Explained
- What are Gaussian Mixture Models? A Powerful Clustering Algorithm
- A Gentle Introduction to Expectation-Maximization (EM Algorithm)
- EM Algorithm In Machine Learning | Expectation-Maximization | Machine Learning Tutorial | Edureka - YouTube
- (ML 16.3) Expectation-Maximization (EM) algorithm - YouTube
- Mixture Models - YouTube
- Expectation Maximization Algorithm - YouTube
- Clustering - YouTube
- Clustering (4): Gaussian Mixture Models and EM - YouTube
- Expectation Maximization with an Example – Stokastik
- ML | Expectation-Maximization Algorithm - GeeksforGeeks
- EM Algorithm (Expectation-maximization): Simple Definition - Statistics How To
- A Tutorial on the Expectation Maximization (EM) Algorithm
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Save bjrp/74f5a06f3d01ce4b745954cfe0f3750e to your computer and use it in GitHub Desktop.
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