3 different types of machine learning
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
How to Learn Machine Learning in 10 Days
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
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?
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?
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?
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
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
How to Select Support Vector Machine Kernels
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?
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
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.