README.md

Coursera-Certificates

1. Advanced Algorithms and Complexity
2. Algorithmic Toolbox
3. Algorithms on Graphs
4. Algorithms on Strings
5. Apply Generative Adversarial Networks (GANs)
6. Architecting Smart IoT Devices
7. Build Basic Generative Adversarial Networks (GANs)
8. Build Better Generative Adversarial Networks (GANs)
9. C++ For C Programmers, Part A
10. Convolutional Neural Networks
11. Data Structures
12. Deep Learning
13. Divide and Conquer, Sorting and Searching, and Randomized Algorithms
14. Generative Adversarial Networks (GANs)
15. Graph Search, Shortest Paths, and Data Structures
16. Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
17. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
18. Internet of Things: Multimedia Technologies
19. Introduction to Architecting Smart IoT Devices
20. Introduction to Self-Driving Cars
21. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
22. Machine Learning Foundations: A Case Study Approach
23. Machine Learning
24. Mathematics for Machine Learning: Linear Algebra
25. Mathematics for Machine Learning: Multivariate_Calculus
26. Mathematics for Machine Learning: PCA
27. Mathematics for Machine Learning Specialization
28. Motion Planning for Self-Driving Cars
29. Neural Networks and Deep Learning
30. Neural Networks for Machine Learning
31. Programming for Everybody (Getting Started with Python)
32. Python Data Structures
33. Robotics: Aerial Robotics
34. Robotics: Computational Motion Planning
35. Robotics: Estimation and Learning
36. Robotics: Perception
37. Self-Driving Cars Specialization
38. Sequence Models
39. Software Architecture for the Internet of Things
40. State Estimation and Localization for Self-Driving Cars
41. Structuring Machine Learning Projects
42. Using Python to Access Web Data
43. Visual Perception for Self-Driving Cars