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

different-ways-to-tune-hyperparameters

3 Different Ways to Tune Hyperparameters (Interactive Python Code)

Feature Selection

6 Ways for Feature Selection

Feature Selection and Data Visualization

How to fix overfitting ?

ONLY 3 ways to fix

1. Reduce Features i.e. w1x1+ w2x2 + w3x3 + …. wmxm decrease m
2. Increase data
3. 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

Covariance and correlation

Matrices

Invertible matrix

Invertible and noninvertibles matrices

Row Echelon form

Row Echelon Form of a Matrix

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

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