In this post I’ll be working up, analyzing, visualizing, and doing Gradient Descent for Linear Regression. It’s a Jupyter notebook with all the code for plots and functions in Python available on my github account.
Machine Learning and Data Science: Linear Regression Part 3
In Part 3 of this series on Linear Regression I will go into more detail about the Model and Cost function. Including several graphs that will hopefully give insight into the their nature and serve as a reference for developing algorithms in the next post.
Machine Learning and Data Science: Linear Regression Part 2
In Part 2 of this series on Linear Regression I will pull a data-set of house sale prices and “features” from Kaggle and explore the data in a Jupyter notebook with pandas and seaborn. We will extract a good subset of data to use for our example analysis of the linear regression algorithms.
Machine Learning and Data Science: Linear Regression Part 1
Linear regression could possibly be considered the “Hello World” problem of Machine Learning. It’s implementation touches on many of the fundamental ideas and problems in this field. I’ll give you some guidance for understanding and implementation of this fundamental idea.
Machine Learning and Data Science: Introduction
This is the start of a series of posts on Machine Learning and Data Science. I’ll be exploring the algorithms and tools of Machine Learning and Data Science. It will be tutorials, guides, how-to, reviews and “real world” application. The post will be done using Juypter notebooks and the notebooks will be available on GitHub.
