📘

ISLR

Statistical Learning Book.

👋

Introduction

Intro to ML.

📈

Regression

Basic Regression.

🔢

LR & LinAlg

Linear Algebra view.

📊

Equation Analysis

Analyzing Regressions.

📉

Gradient Descent

Optimization.

🔘

Logistic Reg

Classification.

🧪

OSL GD MLE

Estimations.

🧩

Clustering

Partitioning Approach.

🌳

Clustering Hierarchical

Density Based Approach.

🌲

Decision Tree

Tree Models.

📍

KNN

K-Nearest Neighbors.

🛡️

SVM

Support Vector Machines.

🔀

Variables Trans

LR transformations.

🧠

Deep Learning

Neural Networks.

🔄

Backpropagation

Training Networks.

🖼️

CNN

ConvNet.

🔁

RNN

Recurrent Networks.

📏

ML Metrics

Evaluation.

📏

Lin Reg 1

Basic Linear Regression.

📏

Lin Reg 2

Advanced LR.

🛡️

LR Assumptions

Theoretical Checks.

📊

ML Metrics

Evaluation old.

📉

P Value

Statistics basics.

🔢

Statistics

Z & T Stats.