rakshith-b / courses

some courses I took.

Courses

This is a memo of some courses I learnt.

1. NTU_Probability-I-II(**大学，叶丙成， 顽想学概率一，二)

• 概率一
• 概率二

2. MachineLearing-Hsuan-Tien-Lin（机器学习基石&技法，**大学，林轩田）

• 机器学习基石：上 , 下
• 机器学习技法：。。。

3. Probabilistic Graphical Models, Stanford University

• Probabilistic Graphical Models 1: Representation
• Probabilistic Graphical Models 2:
• Probabilistic Graphical Models 3:

4. CMU-11-785-Fall-2018, 11-485/785 Introduction to Deep Learning

• Introduction to Deep Learning

5. CMU_PGM_Eric Xing, Probabilistic Graphical Models

• Probabilistic Graphical Models

6. EE364

• EE364a: Convex Optimization I

7. Machine Learning, Andrew Ng

• Machine Learning

8. CS229

• CS229: Machine Learning

9. CS231n

• CS231n: Convolutional Neural Networks for Visual Recognition

10. Deep-Learning-Specialization, Andrew Ng

• Neural Networks and Deep Learning
• Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Structuring Machine Learning Projects
• Convolutional Neural Networks
• Sequence Models

11. Mathematics for Machine Learning

• Linear Algebra
• Multivariate Calculus
• PCA

12. Python for Everybody

• Getting Started with Python
• Python Data Structures
• Using Python to Access Web Data

13. CS 188

• CS 188: Introduction to Artificial Intelligence, Fall 2018

14. CS294-112

• CS 294-112, Deep Reinforcement Learning

15. Machine Learning (2017,Spring), Hung-yi Lee

• Machine Learning (2017,Spring)

16. Machine Learning and having it deep and structured (2018,Spring), Hung-yi Lee

• Machine Learning and having it deep and structured (2018,Spring)

17. Python 3 Programming

• Python Basics

18. Machine Learning 专项课程

• Machine Learning Foundations: A Case Study Approach

19. Advanced Machine Learning 专项课程

• Introduction to Deep Learning

20. Machine Learning with TensorFlow on Google Cloud Platform 专项课程

• How Google does Machine Learning

21. Applied Data Science with Python 专项课程

• Introduction to Data Science in Python