### Machine Learning and Data Science: Multinomial (Multiclass) Logistic Regression

The post will implement Multinomial Logistic Regression. The multiclass approach used will be one-vs-rest. The Jupyter notebook contains a full collection of Python functions for the implementation. An example problem done showing image classification using the MNIST digits dataset.

### Machine Learning and Data Science: Logistic Regression Examples-1

This post will be mostly Python code with implementation and examples of the Logistic Regression theory we have been discussing in the last few posts. Examples include fitting to 2 feature data using an arbitrary order multinomial model and a simple 2 class image classification problem using the MNIST digits data.

### Machine Learning and Data Science: Logistic Regression Implementation

In this post I’ll discuss evaluating the “goodness of fit” for a Logistic Regression model and do an implementation of the formulas in Python using numpy. We’ll look at an example to check the validity of the code.

### Machine Learning and Data Science: Logistic and Linear Regression Regularization

In this post I will look at “Regularization” in order to address an important problem that is common with implementations, namely over-fitting. We’ll go through for logistic regression and linear regression. After getting the equations for regularization worked out we’ll look at an example in Python showing how this can be used for a badly over-fit linear regression model.

### Machine Learning and Data Science: Logistic Regression Theory

Logistic regression is a widely used Machine Learning method for binary classification. It is also a good stepping stone for understanding Neural Networks. In this post I will present the theory behind it including a derivation of the Logistic Regression Cost Function gradient.

### Machine Learning and Data Science: Linear Regression Part 6

This will be the last post in the Linear Regression series. We will look at the problems of over or under fitting data along with non-linear feature variables.

### Machine Learning and Data Science: Linear Regression Part 5

In this post I will present the matrix/vector form of the Linear Regression problem and derive the “exact” solution for the parameters.

### Machine Learning and Data Science: Linear Regression Part 4

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.