
Lecture Description
We continue the topic of clustering and unsupervised machine learning with Mean Shift, this time applying it to our Titanic dataset.
There is some degree of randomness here, so your results may not be the same. You can probably re-run the program to get similar data if you don't get something similar, however.
We're going to take a look at the Titanic dataset via clustering with Mean Shift. What we're interested to know is whether or not Mean Shift will automatically separate passengers into groups or not. If so, it will be interesting to inspect the groups that are created. The first obvious curiosity will be the survival rates of the groups found, but, then, we will also poke into the attributes of these groups to see if we can understand why the Mean Shift algorithm decided on the specific groups.
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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.