Machine Learning Concepts.
Decision Theory / Risk Estimation
Machine Learning #09 Statistical Decision Theory: Regression
OLS Assumptions
7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regression - Statistics By Jim
The Gauss-Markov Theorem and BLUE OLS Coefficient Estimates
The Gauss-Markov Theorem and BLUE OLS Coefficient Estimates - Statistics By Jim
PCA
Making sense of principal component analysis, eigenvectors & eigenvalues
PCA (Overview)
StatQuest: Principal Component Analysis (PCA), Step-by-Step
PCA Math
Principal Component Analysis (The Math) : Data Science Concepts
LDA
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
How does .score() work?
[[Python/Sklearn] How does .score() works? | Data Science and Machine Learning](https://www.kaggle.com/getting-started/27261)
different-ways-to-tune-hyperparameters
3 Different Ways to Tune Hyperparameters (Interactive Python Code)
Feature Selection
Feature Selection and Data Visualization
How to fix overfitting ?
ONLY 3 ways to fix
- Reduce Features i.e. w1x1+ w2x2 + w3x3 + …. wmxm decrease m
- Increase data
- Regularization
Joint and conditional probabilities
Basic probability: Joint, marginal and conditional probability | Independence
Multivariate Gaussian distributions and geometry
Multivariate Gaussian distributions
Multinomial Gaussian distribution
Covariance correlation
Covariance is unit less
Simple explanation: Covariance vs Correlation?
Covariance Matrix
The Covariance Matrix : Data Science Basics
Difference between Covariance and Correlation
Matrices
Invertible matrix
Invertible and noninvertibles matrices
Row Echelon form
Gaussian Elimination & Row Echelon Form
Gaussian Elimination & Row Echelon Form
Expectation formulas
SVM Support Vector Machines
16. Learning: Support Vector Machines
Logistic Regression
Machine Learning Lecture 11 “Logistic Regression” -Cornell CS4780 SP17
