**How to Learn Machine Learning in 10 Days** - 01 May 2017

10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.

**Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed** - 25 Nov 2016

Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.

**What is the Role of the Activation Function in a Neural Network?** - 30 Aug 2016

Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.

**What is Softmax Regression and How is it Related to Logistic Regression?** - 01 Jul 2016

An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.

**Regularization in Logistic Regression: Better Fit and Better Generalization?** - 24 Jun 2016

A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.

**A Visual Explanation of the Back Propagation Algorithm for Neural Networks** - 17 Jun 2016

A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.

**How to Select Support Vector Machine Kernels** - 13 Jun 2016

Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.

**What is the Difference Between Deep Learning and “Regular” Machine Learning?** - 03 Jun 2016

Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.

**A Concise Overview of Standard Model-fitting Methods** - 27 May 2016

A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.

**How to Explain Machine Learning to a Software Engineer** - 20 May 2016

How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.