
Lecture Description
Welcome to the 8th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Where we left off, we had just realized that we needed to replicate some non-trivial algorithms into Python code in an attempt to calculate a best-fit line for a given dataset.
Before we embark on that, why are we going to bother with all of this? Linear Regression is basically the brick to the machine learning building. It is used in almost every single major machine learning algorithm, so an understanding of it will help you to get the foundation for most major machine learning algorithms. For the enthusiastic among us, understanding linear regression and general linear algebra is the first step towards writing your own custom machine learning algorithms and branching out into the bleeding edge of machine learning, using what ever the best processing is at the time. As processing improves and hardware architecture changes, the methodologies used for machine learning also change. The more recent rise in neural networks has had much to do with general purpose graphics processing units. Ever wonder what's at the heart of an artificial neural network? You guessed it: linear regression.
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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.