
Lecture Description
Welcome to the 10th part of our of our machine learning regression tutorial within our Machine Learning with Python tutorial series. We've just recently finished creating a working linear regression model, and now we're curious what is next. Right now, we can easily look at the data, and decide how "accurate" the regression line is to some degree. What happens, however, when your linear regression model is applied within 20 hierarchical layers in a neural network? Not only this, but your model works in steps, or windows, of say 100 data points at a time, within a dataset of 5 million datapoints. You're going to need some sort of automated way of discovering how good your best fit line actually is.
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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.