ML and DL - Section 1 : Introduction

강의 : udemy (https://www.udemy.com/course/introduction-to-machine-learning-in-python/)

 

Course Flow

 

1. Fundamental basics

 

<Machine Learning>

 

2. Linear Regression & Logistic Regression

 

3. K-Nearest Neighbor Classifier

 

4. Naive Bayes Classifier

 

5. SVMs (Support Vector Machines)

 

6. Decision Trees

 

7. Random Forest Classifier

 

8. Boosting (Computer Vision Algorithm)

 

9. Principal Component Analysis (PCA)

 

10. Clustering Algorithm

 

11. Face Detection

 

12. Face Recognition

 

<Deep Learning>

 

13. Feed-Forward Neural Networks

 

14. Deep Neural Networks

 

15. Convolutional Neural Networks

 

16. Recurrent Neural Networks (RNNs)

 

<Reinforcement Learning>

 

17. Markov Decision Processes (MDP)

 

18. Exploration vs Exploitation Problem

 

19. Q Learning Theory

 

20. Q Learning Implementation (Tic Tac Toe)

 

21. Deep Q Learning Theory

 

22. Deep Q Learning Implementation (Tic Tac Toe)

 

IDE : 파이참