We've been learning about regression, and even coded our own very simple linear regression algorithm. Along with that, we've also built a coefficient of determination algorithm to check for the accuracy and reliability of our best-fit line. We've discussed and shown how a best-fit line may not be a great fit, but also explained why our example was correct directionally, even if it was not exact. Now, however, we are at the point where we're using two top-level algorithms, which are subsequently comprised of a handful of smaller algorithms. As we continue building this hierarchy of algorithms, we might wind up finding ourselves in trouble if just one of them have a tiny error, so we want to test our assumptions.

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1. Introduction to Machine Learning
2. Regression Intro
3. Regression Features and Labels
4. Regression Training and Testing
5. Regression forecasting and predicting
6. Pickling and Scaling
7. Regression How it Works
8. How to program the Best Fit Slope
9. How to program the Best Fit Line
10. R Squared Theory
11. Programming R Squared
12. Testing Assumptions
13. Classification w/ K Nearest Neighbors Intro
14. K Nearest Neighbors Application
15. Euclidean Distance
16. Creating Our K Nearest Neighbors A
17. Writing our own K Nearest Neighbors in Code
18. Applying our K Nearest Neighbors Algorithm
19. Final thoughts on K Nearest Neighbors
20. Support Vector Machine Intro and Application
21. Understanding Vectors
22. Support Vector Assertion
23. Support Vector Machine Fundamentals
24. Support Vector Machine Optimization
25. Creating an SVM from scratch
26. SVM Training
27. SVM Optimization
28. Completing SVM from Scratch
29. Kernels Introduction
30. Why Kernels
31. Soft Margin SVM
32. Soft Margin SVM and Kernels with CVXOPT
33. SVM Parameters
34. Clustering Introduction
35. Handling Non-Numeric Data
36. K Means with Titanic Dataset
37. Custom K Means
38. K Means from Scratch
39. Mean Shift Intro
40. Mean Shift with Titanic Dataset
41. Mean Shift from Scratch
42. Mean Shift Dynamic Bandwidth

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.