Machine Learning Fundamentals

Knowledge Tree

Follow the learning path through 27 essential Machine Learning topics

1

Basics

Linear Regression

2

Basics

K-means Clustering

3

Basics

K-means Applications

4

Basics

K-nearest Neighbors

5

Optimization

Gradient Descent (1/2)

6

Optimization

Gradient Descent (2/2)

7

Neural Networks

Perceptron Learning Algorithm

8

Classification

Logistic Regression

9

Preprocessing

Feature Engineering

10

Classification

Binary Classifiers

11

Classification

Softmax Regression

12

Neural Networks

MLP & Backpropagation

13

Optimization

Overfitting

14

Optimization

Convex Sets & Functions

15

Optimization

Convex Optimization

16

Optimization

Duality

17

SVM

Support Vector Machine

18

SVM

Soft Margin SVM

19

SVM

Kernel SVM

20

SVM

Multi-class SVM

21

Recommender Systems

Content-based RecSys

22

Recommender Systems

Collaborative Filtering

23

Recommender Systems

Matrix Factorization

24

Dimensionality Reduction

SVD

25

Dimensionality Reduction

PCA (1/2)

26

Dimensionality Reduction

PCA (2/2)

27

Dimensionality Reduction

Linear Discriminant Analysis