
Lecture Description
In this tutorial, we cover the Soft Margin SVM, along with Kernels and quadratic programming with CVXOPT all in one quick tutorial using some example code from:
www.mblondel.org/journal/2010/09/19/support-vector-machines-in-python/
Visualizing the conversion of many dimensions back to 2D: www.youtube.com/watch?v=3liCbRZPrZA
Quadratic programming with CVXOPT: cvxopt.org/userguide/coneprog.html#quadratic-programming
Docs qp example: cvxopt.org/examples/tutorial/qp.html
Another CVXOPT tutorial: courses.csail.mit.edu/6.867/wiki/images/a/a7/Qp-cvxopt.pdf
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Course Index
- Introduction to Machine Learning
- Regression Intro
- Regression Features and Labels
- Regression Training and Testing
- Regression forecasting and predicting
- Pickling and Scaling
- Regression How it Works
- How to program the Best Fit Slope
- How to program the Best Fit Line
- R Squared Theory
- Programming R Squared
- Testing Assumptions
- Classification w/ K Nearest Neighbors Intro
- K Nearest Neighbors Application
- Euclidean Distance
- Creating Our K Nearest Neighbors A
- Writing our own K Nearest Neighbors in Code
- Applying our K Nearest Neighbors Algorithm
- Final thoughts on K Nearest Neighbors
- Support Vector Machine Intro and Application
- Understanding Vectors
- Support Vector Assertion
- Support Vector Machine Fundamentals
- Support Vector Machine Optimization
- Creating an SVM from scratch
- SVM Training
- SVM Optimization
- Completing SVM from Scratch
- Kernels Introduction
- Why Kernels
- Soft Margin SVM
- Soft Margin SVM and Kernels with CVXOPT
- SVM Parameters
- Clustering Introduction
- Handling Non-Numeric Data
- K Means with Titanic Dataset
- Custom K Means
- K Means from Scratch
- Mean Shift Intro
- Mean Shift with Titanic Dataset
- Mean Shift from Scratch
- Mean Shift Dynamic Bandwidth
Course Description
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.